Main Program (Notebook Version).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Confirmation-Bias-Analyser/Main-Program/blob/main/Main%20Program%20(Notebook_Version).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "hWfyZWp7rj7E"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install sentence_transformers\n",
        "!pip install vaderSentiment\n",
        "!pip install anytree\n",
        "!pip install dash\n",
        "!pip install jupyter-dash\n",
        "!pip install pyvis"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import essential libraries and functions"
      ],
      "metadata": {
        "id": "doYpuVELNMSi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "CJjKKLkkW8Bg"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification, InputExample, InputFeatures\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from google.colab import files\n",
        "import random\n",
        "import re\n",
        "\n",
        "# The shutil module offers a number of high-level operations on files and collections of files.\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "import sys\n",
        "mainDirectory = \"/content/drive/MyDrive/Final Year Project/Key Notebooks/Main Program/\"\n",
        "sys.path.append(mainDirectory + 'Functions/')\n",
        "from confirmation_bias_model_functions import *\n",
        "from data_collection_functions import *\n",
        "from data_preprocessing_functions import *\n",
        "from visualisation_functions import *\n",
        "from verification_functions import *\n",
        "\n",
        "# from textblob import TextBlob\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "from wordcloud import STOPWORDS\n",
        "\n",
        "from jupyter_dash import JupyterDash\n",
        "from dash import Dash, html, Input, Output, dash_table, dcc\n",
        "from dash.dependencies import Input, Output\n",
        "from IPython.core.display import display, HTML"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Authenticate API keys"
      ],
      "metadata": {
        "id": "P8OgZp1eJcjx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "G1SApui7aOAQ"
      },
      "outputs": [],
      "source": [
        "with open(mainDirectory + 'twitter_bearer_token.txt', 'r', encoding=\"utf8\") as f:\n",
        "    token = f.read()\n",
        "\n",
        "header = create_Twitter_headers(token)\n",
        "\n",
        "with open(mainDirectory + 'database_uri.txt', 'r', encoding=\"utf8\") as f:\n",
        "    uri = f.read()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHhJvTa2msJo"
      },
      "source": [
        "# Data Collection"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read the data and present as a dataframe"
      ],
      "metadata": {
        "id": "sYkxgFr6KtmI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_for_analysis = ['1522931750451617793', '1507922082683793408', '553553331671408641']\n",
        "\n",
        "for i in range(len(tweets_for_analysis)):\n",
        "    print(i, tweets_for_analysis[i])\n",
        "\n",
        "tweetOption = 0 #int(input(\"Please indicate the tweet to analyse.\")) - 1\n",
        "\n",
        "print(f'\\nChosen {tweets_for_analysis[tweetOption]}\\n')\n",
        "\n",
        "if tweetOption == 0 or tweetOption == 1:\n",
        "    conversationID = tweets_for_analysis[tweetOption]\n",
        "\n",
        "    query = '''\n",
        "        select * from comments_for_analysis where conversation_id = '%s'\n",
        "    '''% conversationID\n",
        "    \n",
        "    df = getData(query, uri)\n",
        "\n",
        "    parent = df['head_id'][0]\n",
        "\n",
        "    print('About the tweet:')\n",
        "    print(getSingleTweetInfo(conversationID, header)['data'][0]['text'])\n",
        "\n",
        "    print('Number of comments:', len(df))\n",
        "\n",
        "elif tweetOption == 2:\n",
        "    conversationID = tweets_for_analysis[tweetOption]\n",
        "\n",
        "    query = '''\n",
        "        select * from pheme_dataset_for_analysis where head_id = '%s'\n",
        "    '''% parent\n",
        "\n",
        "    df = getData(query, uri)\n",
        "\n",
        "    print('Number of comments:', len(df))\n",
        "\n",
        "# Create directory to save all results\n",
        "os.mkdir(tweets_for_analysis[tweetOption])\n",
        "\n",
        "df['url'] = df['comment'].apply(lambda x: getLinks(x))\n",
        "df['link_title'] = df['url'].apply(lambda x: getURLfromList(x))\n",
        "df.head()    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        },
        "id": "VQMojddoHlA4",
        "outputId": "a556b32c-3faf-46d6-e690-5649d56acb08"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 1522931750451617793\n",
            "1 1507922082683793408\n",
            "2 553553331671408641\n",
            "\n",
            "Chosen 1522931750451617793\n",
            "\n",
            "About the tweet:\n",
            "Over 100 people protest against Covid-19 vaccines with Healing the Divide founder Iris Koh https://t.co/QrY9DhO9sF https://t.co/x1C47MOzUm\n",
            "Number of comments: 41\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                 timestamp    reply_to  \\\n",
              "0  10038  2022-05-07T13:40:35.000Z  1619325942   \n",
              "1  10000  2022-05-08T15:09:14.000Z       10038   \n",
              "2  10001  2022-05-08T11:36:53.000Z  1619325942   \n",
              "3  10002  2022-05-08T11:30:17.000Z  1619325942   \n",
              "4  10003  2022-05-08T11:21:23.000Z  1619325942   \n",
              "\n",
              "                                             comment social_media     head_id  \\\n",
              "0  @MothershipSG Why were they not arrested for i...      Twitter  1619325942   \n",
              "1  @zacroix @MothershipSG Is about time for peopl...      Twitter  1619325942   \n",
              "2  @MothershipSG Lanjiao ah! Healing the divide b...      Twitter  1619325942   \n",
              "3                        @MothershipSG She's a clown      Twitter  1619325942   \n",
              "4  @MothershipSG For every protest in any country...      Twitter  1619325942   \n",
              "\n",
              "       conversation_id              user_id url link_title  \n",
              "0  1522931750451617793           1474416398                 \n",
              "1  1522931750451617793  1520451553165193216                 \n",
              "2  1522931750451617793  1348988297205305345                 \n",
              "3  1522931750451617793   770445136371167232                 \n",
              "4  1522931750451617793  1516288928047071233                 "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bea19564-ed7e-4920-9c2d-f69d15bd3ac9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>reply_to</th>\n",
              "      <th>comment</th>\n",
              "      <th>social_media</th>\n",
              "      <th>head_id</th>\n",
              "      <th>conversation_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>url</th>\n",
              "      <th>link_title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10038</td>\n",
              "      <td>2022-05-07T13:40:35.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG Why were they not arrested for i...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1474416398</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "      <td>2022-05-08T15:09:14.000Z</td>\n",
              "      <td>10038</td>\n",
              "      <td>@zacroix @MothershipSG Is about time for peopl...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1520451553165193216</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>2022-05-08T11:36:53.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG Lanjiao ah! Healing the divide b...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1348988297205305345</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10002</td>\n",
              "      <td>2022-05-08T11:30:17.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG She's a clown</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>770445136371167232</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10003</td>\n",
              "      <td>2022-05-08T11:21:23.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG For every protest in any country...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1516288928047071233</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bea19564-ed7e-4920-9c2d-f69d15bd3ac9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bea19564-ed7e-4920-9c2d-f69d15bd3ac9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bea19564-ed7e-4920-9c2d-f69d15bd3ac9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "d92GdbPhQc-B"
      },
      "outputs": [],
      "source": [
        "# with open(mainDirectory + 'database_uri.txt', 'r', encoding=\"utf8\") as f:\n",
        "#     uri = f.read()\n",
        "\n",
        "# socialMedia = ['Reddit', 'Twitter', 'PHEME Dataset']\n",
        "\n",
        "# for i in range(len(socialMedia)):\n",
        "#     print(i, socialMedia[i])\n",
        "\n",
        "# socialMediaOption = 1\n",
        "\n",
        "# print(f'\\nChosen {socialMedia[socialMediaOption]}\\n')\n",
        "\n",
        "# if socialMedia[socialMediaOption] == 'Reddit':\n",
        "#     parent = 'rmqevj'\n",
        "#     query = '''\n",
        "#         select * from reddit_posts_for_analysis where head_id = '%s'\n",
        "#     '''% parent\n",
        "#     df = getData(query, uri)\n",
        "    \n",
        "# elif socialMedia[socialMediaOption] == 'Twitter':\n",
        "#     # conversationID = '1522931750451617793'\n",
        "#     conversationID = '1507922082683793408'\n",
        "\n",
        "#     query = '''\n",
        "#         select * from comments_for_analysis where conversation_id = '%s'\n",
        "#     '''% conversationID\n",
        "    \n",
        "#     df = getData(query, uri)\n",
        "\n",
        "#     parent = df['head_id'][0]\n",
        "\n",
        "#     print('About the tweet:')\n",
        "#     print(getSingleTweetInfo(conversationID, header)['data'][0]['text'])\n",
        "\n",
        "#     print('Number of comments:', len(df))\n",
        "\n",
        "# elif socialMedia[socialMediaOption] == 'PHEME Dataset':\n",
        "#     parent = '553553331671408641'\n",
        "#     query = '''\n",
        "#         select * from pheme_dataset_for_analysis where head_id = '%s'\n",
        "#     '''% parent\n",
        "#     df = getData(query, uri)\n",
        "\n",
        "# df['url'] = df['comment'].apply(lambda x: getLinks(x))\n",
        "# df['link_title'] = df['url'].apply(lambda x: getURLfromList(x))\n",
        "# df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Construction of tree of comments"
      ],
      "metadata": {
        "id": "UlGAaDqsKEcQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "0SA8N4lmQdI4"
      },
      "outputs": [],
      "source": [
        "root = Node(parent)\n",
        "\n",
        "input_list = [] \n",
        "\n",
        "item_count = df['reply_to'].value_counts().to_dict()\n",
        "\n",
        "for i in range(len(df['id'].tolist())):\n",
        "    try:\n",
        "      if df['id'].loc[i] != df['reply_to'].loc[i]:\n",
        "          input_list.append((df['id'].loc[i], df['reply_to'].loc[i]))\n",
        "\n",
        "    except:\n",
        "      continue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "jEzZFu5LQdGK"
      },
      "outputs": [],
      "source": [
        "output_dict = make_map(input_list)\n",
        "createTweetsTree(output_dict[parent], root)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MHxqfFO6myiX"
      },
      "source": [
        "# Sentiment Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0WnKFzsXi0kf"
      },
      "outputs": [],
      "source": [
        "pred_sentences = cleanComments(df['comment'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "g3nUeAKC8fLZ"
      },
      "outputs": [],
      "source": [
        "main_directory = '/content/drive/MyDrive/Final Year Project/Key Notebooks/Main Program/'\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(main_directory + \"subjectivity_tokenizer/\")\n",
        "model = TFBertForSequenceClassification.from_pretrained(main_directory + \"saved_subjectivity_model/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "eYR5nNqp82GB"
      },
      "outputs": [],
      "source": [
        "textblob_polarity = []\n",
        "textblob_subjectivity = []\n",
        "vader_results = []\n",
        "vaderCompoundScores = []\n",
        "model_subjectivity_result = predictFromModel(model, tokenizer, pred_sentences)\n",
        "\n",
        "for i in pred_sentences:\n",
        "  result = getSentimentalResults(i)\n",
        "  textblob_polarity.append(result['textblob_polarity'])\n",
        "  textblob_subjectivity.append(result['textblob_subjectivity'])\n",
        "  vader_results.append(result['vader_results'])\n",
        "  vaderCompoundScores.append(result['vader_compound_scores'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving of results from sentiment analysis into the same dataframe"
      ],
      "metadata": {
        "id": "TDMlo4uJKpCo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "59Yu1TVUrVA-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "131c6f01-ab65-4ba1-d93c-cb61b2b98c82"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                 timestamp    reply_to  \\\n",
              "0  10038  2022-05-07T13:40:35.000Z  1619325942   \n",
              "1  10000  2022-05-08T15:09:14.000Z       10038   \n",
              "2  10001  2022-05-08T11:36:53.000Z  1619325942   \n",
              "3  10002  2022-05-08T11:30:17.000Z  1619325942   \n",
              "4  10003  2022-05-08T11:21:23.000Z  1619325942   \n",
              "\n",
              "                                             comment social_media     head_id  \\\n",
              "0  @MothershipSG Why were they not arrested for i...      Twitter  1619325942   \n",
              "1  @zacroix @MothershipSG Is about time for peopl...      Twitter  1619325942   \n",
              "2  @MothershipSG Lanjiao ah! Healing the divide b...      Twitter  1619325942   \n",
              "3                        @MothershipSG She's a clown      Twitter  1619325942   \n",
              "4  @MothershipSG For every protest in any country...      Twitter  1619325942   \n",
              "\n",
              "       conversation_id              user_id url link_title  ...  \\\n",
              "0  1522931750451617793           1474416398                 ...   \n",
              "1  1522931750451617793  1520451553165193216                 ...   \n",
              "2  1522931750451617793  1348988297205305345                 ...   \n",
              "3  1522931750451617793   770445136371167232                 ...   \n",
              "4  1522931750451617793  1516288928047071233                 ...   \n",
              "\n",
              "   textblob_polarity  vader_compound_score  vader_polarity overall_polarity  \\\n",
              "0             -0.500                0.6681             POS          UNKNOWN   \n",
              "1              0.125               -0.4364             NEG          UNKNOWN   \n",
              "2              0.500                0.0000             NEU          UNKNOWN   \n",
              "3              0.000                0.0000             NEU              NEU   \n",
              "4              0.000               -0.2500             NEU              NEU   \n",
              "\n",
              "  model_subjectivity  textblob_subjectivity  overall_subjectivity  \\\n",
              "0           0.000876                   0.50             OBJECTIVE   \n",
              "1           0.003720                   0.45             OBJECTIVE   \n",
              "2           0.003858                   0.50             OBJECTIVE   \n",
              "3           0.003196                   0.00             OBJECTIVE   \n",
              "4           0.003846                   0.00             OBJECTIVE   \n",
              "\n",
              "                            vader_sentiment topic_cluster  potential_bias  \n",
              "0  {'neg': 0.0, 'neu': 0.523, 'pos': 0.477}             1               0  \n",
              "1  {'neg': 0.121, 'neu': 0.879, 'pos': 0.0}             1               0  \n",
              "2      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0}             1               0  \n",
              "3      {'neg': 0.0, 'neu': 1.0, 'pos': 0.0}             1               0  \n",
              "4  {'neg': 0.143, 'neu': 0.857, 'pos': 0.0}             1               0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8718ed5-1a82-4a2a-8e10-becb03a63478\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>reply_to</th>\n",
              "      <th>comment</th>\n",
              "      <th>social_media</th>\n",
              "      <th>head_id</th>\n",
              "      <th>conversation_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>url</th>\n",
              "      <th>link_title</th>\n",
              "      <th>...</th>\n",
              "      <th>textblob_polarity</th>\n",
              "      <th>vader_compound_score</th>\n",
              "      <th>vader_polarity</th>\n",
              "      <th>overall_polarity</th>\n",
              "      <th>model_subjectivity</th>\n",
              "      <th>textblob_subjectivity</th>\n",
              "      <th>overall_subjectivity</th>\n",
              "      <th>vader_sentiment</th>\n",
              "      <th>topic_cluster</th>\n",
              "      <th>potential_bias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10038</td>\n",
              "      <td>2022-05-07T13:40:35.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG Why were they not arrested for i...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1474416398</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>...</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>0.6681</td>\n",
              "      <td>POS</td>\n",
              "      <td>UNKNOWN</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.50</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>{'neg': 0.0, 'neu': 0.523, 'pos': 0.477}</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "      <td>2022-05-08T15:09:14.000Z</td>\n",
              "      <td>10038</td>\n",
              "      <td>@zacroix @MothershipSG Is about time for peopl...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1520451553165193216</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>...</td>\n",
              "      <td>0.125</td>\n",
              "      <td>-0.4364</td>\n",
              "      <td>NEG</td>\n",
              "      <td>UNKNOWN</td>\n",
              "      <td>0.003720</td>\n",
              "      <td>0.45</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>{'neg': 0.121, 'neu': 0.879, 'pos': 0.0}</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>2022-05-08T11:36:53.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG Lanjiao ah! Healing the divide b...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1348988297205305345</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>...</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NEU</td>\n",
              "      <td>UNKNOWN</td>\n",
              "      <td>0.003858</td>\n",
              "      <td>0.50</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0}</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10002</td>\n",
              "      <td>2022-05-08T11:30:17.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG She's a clown</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>770445136371167232</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NEU</td>\n",
              "      <td>NEU</td>\n",
              "      <td>0.003196</td>\n",
              "      <td>0.00</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0}</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10003</td>\n",
              "      <td>2022-05-08T11:21:23.000Z</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>@MothershipSG For every protest in any country...</td>\n",
              "      <td>Twitter</td>\n",
              "      <td>1619325942</td>\n",
              "      <td>1522931750451617793</td>\n",
              "      <td>1516288928047071233</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.2500</td>\n",
              "      <td>NEU</td>\n",
              "      <td>NEU</td>\n",
              "      <td>0.003846</td>\n",
              "      <td>0.00</td>\n",
              "      <td>OBJECTIVE</td>\n",
              "      <td>{'neg': 0.143, 'neu': 0.857, 'pos': 0.0}</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8718ed5-1a82-4a2a-8e10-becb03a63478')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b8718ed5-1a82-4a2a-8e10-becb03a63478 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b8718ed5-1a82-4a2a-8e10-becb03a63478');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df['number_of_links'] = df['link_title'].apply(lambda x: len(x))\n",
        "\n",
        "# Polarity\n",
        "df['textblob_polarity'] = textblob_polarity\n",
        "df['vader_compound_score'] = vaderCompoundScores\n",
        "df['vader_polarity'] = df['vader_compound_score'].apply(lambda x: polarityDetermination(x))\n",
        "\n",
        "overall_polarity = []\n",
        "overall_polarity_scores = {}\n",
        "\n",
        "for i in range(len(textblob_polarity)):\n",
        "  overall_polarity.append(definePolarity(textblob_polarity[i], vaderCompoundScores[i]))\n",
        "  overall_polarity_scores[df['id'][i]] = [textblob_polarity[i], vaderCompoundScores[i]]\n",
        "\n",
        "df['overall_polarity'] = overall_polarity\n",
        "\n",
        "# Subjectivity\n",
        "df['model_subjectivity'] = model_subjectivity_result\n",
        "df['textblob_subjectivity'] = textblob_subjectivity\n",
        "\n",
        "overall_subjectivity = []\n",
        "overall_subjectivity_scores = {}\n",
        "\n",
        "for i in range(len(model_subjectivity_result)):\n",
        "  overall_subjectivity.append(defineSubjectivity(model_subjectivity_result[i], textblob_subjectivity[i]))\n",
        "  overall_subjectivity_scores[df['id'][i]] = [float(model_subjectivity_result[i]), textblob_subjectivity[i]]\n",
        "\n",
        "df['overall_subjectivity'] = overall_subjectivity\n",
        "\n",
        "df['vader_sentiment'] = vader_results\n",
        "df['topic_cluster'] = getClusters(pred_sentences, embedder)\n",
        "\n",
        "df['potential_bias'] = flagPotentialBias(df)\n",
        "\n",
        "df.to_csv(f'{tweets_for_analysis[tweetOption]}/sentiment_result.csv', index=False)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results from sentiment analysis"
      ],
      "metadata": {
        "id": "938LTWg9Jx4n"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "j0yrFfQevFHA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "1996327a-0572-4e83-a62b-76ce62895502"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                                            comment  \\\n",
              "0  10038  @MothershipSG Why were they not arrested for i...   \n",
              "1  10000  @zacroix @MothershipSG Is about time for peopl...   \n",
              "2  10001  @MothershipSG Lanjiao ah! Healing the divide b...   \n",
              "3  10002                        @MothershipSG She's a clown   \n",
              "4  10003  @MothershipSG For every protest in any country...   \n",
              "\n",
              "   textblob_polarity  vader_compound_score vader_polarity  model_subjectivity  \\\n",
              "0             -0.500                0.6681            POS            0.000876   \n",
              "1              0.125               -0.4364            NEG            0.003720   \n",
              "2              0.500                0.0000            NEU            0.003858   \n",
              "3              0.000                0.0000            NEU            0.003196   \n",
              "4              0.000               -0.2500            NEU            0.003846   \n",
              "\n",
              "   textblob_subjectivity  topic_cluster  potential_bias  \n",
              "0                   0.50              1               0  \n",
              "1                   0.45              1               0  \n",
              "2                   0.50              1               0  \n",
              "3                   0.00              1               0  \n",
              "4                   0.00              1               0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8049294-7307-46a6-b716-338a41cba9c2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>comment</th>\n",
              "      <th>textblob_polarity</th>\n",
              "      <th>vader_compound_score</th>\n",
              "      <th>vader_polarity</th>\n",
              "      <th>model_subjectivity</th>\n",
              "      <th>textblob_subjectivity</th>\n",
              "      <th>topic_cluster</th>\n",
              "      <th>potential_bias</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10038</td>\n",
              "      <td>@MothershipSG Why were they not arrested for i...</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>0.6681</td>\n",
              "      <td>POS</td>\n",
              "      <td>0.000876</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000</td>\n",
              "      <td>@zacroix @MothershipSG Is about time for peopl...</td>\n",
              "      <td>0.125</td>\n",
              "      <td>-0.4364</td>\n",
              "      <td>NEG</td>\n",
              "      <td>0.003720</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10001</td>\n",
              "      <td>@MothershipSG Lanjiao ah! Healing the divide b...</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NEU</td>\n",
              "      <td>0.003858</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10002</td>\n",
              "      <td>@MothershipSG She's a clown</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>NEU</td>\n",
              "      <td>0.003196</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10003</td>\n",
              "      <td>@MothershipSG For every protest in any country...</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.2500</td>\n",
              "      <td>NEU</td>\n",
              "      <td>0.003846</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d8049294-7307-46a6-b716-338a41cba9c2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d8049294-7307-46a6-b716-338a41cba9c2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d8049294-7307-46a6-b716-338a41cba9c2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df[['id','comment','textblob_polarity','vader_compound_score','vader_polarity','model_subjectivity','textblob_subjectivity','topic_cluster','potential_bias']].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FjiwXxehtLZ6"
      },
      "source": [
        "# Confirmation Bias Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "lx9LO_Sksy4b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52164192-3541-4e94-b231-4e94fc655b90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "head_thread = parent #input('Enter a comment to look at the replies. ')\n",
        "conversationDF, conversationTree = traceConversation(df, root, head_thread, False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xm4w-2t68dr9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78eedccd-b489-48b9-e599-b59f3062193a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "About the tweet\n",
            "\n",
            "Existence of links:\n",
            "['twitter.com']\n",
            "['twitter.com']\n",
            "['t.me']\n",
            "['twitter.com']\n",
            "['amp.theguardian.com']\n",
            "\n",
            "The exact link in the comments:\n",
            "https://twitter.com/DoctorStone96/status/1523003027195850752/photo/1;\n",
            "https://twitter.com/xandermateusz/status/1522983062346166272/photo/1;\n",
            "https://t.me/childcovidvaccineinjuriesuk;\n",
            "https://twitter.com/Ach1llesHeel/status/1522950563167555590/photo/1;\n",
            "https://amp.theguardian.com/environment/2022/apr/06/microplastics-found-deep-in-lungs-of-living-people-for-first-time;\n"
          ]
        }
      ],
      "source": [
        "print(\"About the tweet\\n\")\n",
        "\n",
        "print(\"Existence of links:\")\n",
        "checkLink = False\n",
        "\n",
        "for i in df['link_title']:\n",
        "    if isinstance(i, list):\n",
        "        print(i)\n",
        "        checkLink = False\n",
        "\n",
        "if checkLink:\n",
        "    print(\"No links\")\n",
        "\n",
        "else:\n",
        "  print(\"\\nThe exact link in the comments:\")\n",
        "  for i in df['url']:\n",
        "      if len(i) > 0:\n",
        "          print(i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "a4oHM11UC2Jk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ec0fa9b-8c6e-4aa3-f812-4643ed181583"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversation Tree\n",
            "1619325942\n",
            "├── 10038\n",
            "│   ├── 10000\n",
            "│   └── 10021\n",
            "├── 10001\n",
            "├── 10002\n",
            "├── 10003\n",
            "├── 10004\n",
            "├── 10008\n",
            "├── 10009\n",
            "├── 10010\n",
            "├── 10011\n",
            "├── 10017\n",
            "├── 10019\n",
            "├── 10020\n",
            "├── 10022\n",
            "├── 10023\n",
            "├── 10024\n",
            "├── 10025\n",
            "├── 10026\n",
            "├── 10028\n",
            "├── 10030\n",
            "├── 10031\n",
            "├── 10033\n",
            "├── 10035\n",
            "├── 10036\n",
            "├── 10037\n",
            "├── 10039\n",
            "│   └── 10034\n",
            "└── 10040\n",
            "    └── 10016\n"
          ]
        }
      ],
      "source": [
        "print(\"Conversation Tree\")\n",
        "printGraph(root)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Results of confirmation bias analysis"
      ],
      "metadata": {
        "id": "Cyx4dzByK3i9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "serfbCYKuKrC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5ba0222-95d8-40ed-ba88-845567b43c3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confirmation Bias Score for Entire Conversation: 0.7714285714285714\n",
            "Number of potentially bias comments: 11\n"
          ]
        }
      ],
      "source": [
        "print(\"Confirmation Bias Score for Entire Conversation:\", calculateBias(conversationDF))\n",
        "print(\"Number of potentially bias comments:\", len(conversationDF[conversationDF['potential_bias'] == 1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "062vxKtVjsYQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "outputId": "6f7d897a-66a8-4de4-e663-62ce8303d467"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "    const iframe = document.createElement('iframe');\n",
              "    iframe.src = new URL(path, url).toString();\n",
              "    iframe.height = height;\n",
              "    iframe.width = width;\n",
              "    iframe.style.border = 0;\n",
              "    element.appendChild(iframe);\n",
              "  })(8050, \"/\", \"100%\", 650, false, window.element)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "scoreLists = ['number_of_links', 'vader_compound_score', 'textblob_polarity', 'textblob_subjectivity', 'model_subjectivity', 'overall_subjectivity', 'overall_polarity', 'topic_cluster', 'potential_bias']\n",
        "df_for_visualisation = conversationDF[['id','timestamp','reply_to','comment'] + scoreLists]\n",
        "\n",
        "app = JupyterDash(__name__)\n",
        "\n",
        "app.layout = html.Div([\n",
        "    html.H4('Conversation'),\n",
        "    html.P(id='table_out'),\n",
        "    dash_table.DataTable(\n",
        "        id='table',\n",
        "        columns=[{\"name\": i, \"id\": i} \n",
        "                 for i in ['id'] + scoreLists],\n",
        "        data=df_for_visualisation.to_dict('records'),\n",
        "        style_cell={\n",
        "            'textAlign': 'center',\n",
        "        },\n",
        "        style_as_list_view=True,\n",
        "        style_header=dict(backgroundColor=\"paleturquoise\"),\n",
        "        style_data=dict(backgroundColor=\"lavender\"),\n",
        "    ), \n",
        "])\n",
        "\n",
        "@app.callback(\n",
        "    Output('table_out', 'children'), \n",
        "    Input('table', 'active_cell'))\n",
        "\n",
        "def update_graphs(active_cell):\n",
        "    if active_cell:\n",
        "        identifier = df_for_visualisation.iloc[active_cell['row']]['id']\n",
        "        comment = df_for_visualisation.iloc[active_cell['row']]['comment']\n",
        "\n",
        "        return f\"Selected ID: {identifier}, Comment by user: \\\"{comment}\\\"\" #, {score}\"\n",
        "    return \"Click the table\"\n",
        "\n",
        "app.run_server(mode='inline')#debug=True"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## List of potentially bias comments"
      ],
      "metadata": {
        "id": "4fG2F2lCK7ia"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "BKr3bkhm2sOz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50961a4b-65e7-4621-c1b2-69b782bd1564"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10008 @MothershipSG What is there to protest now no need to trace together u can go anywhere u want too free these people\n",
            "10011 @MothershipSG Not surprising.\n",
            "It's this category of people who would vote for Donald Trump and Bombong Marcos.\n",
            "They would probably declare that Russia's invasion of Ukraine is justified.\n",
            "They're breathing the wrong air in the wrong country.\n",
            "10016 @K31thc0m @MothershipSG Oh yeah, I don't need to safe entry to most of not all places... The ship has sailed ATM. Which is probably why the permit was granted in the first place. Ok la now I give you talk.\n",
            "10017 @MothershipSG Well, lets see what latest research says about vaccine vs Omicron: 💁‍♂️ https://t.co/PY8EbJQS6Y\n",
            "10019 @MothershipSG What a joke. 100 plus people out of 5.68 million people in Singapore. As if that’s going to make a difference. Blind leading the blind.\n",
            "10020 @MothershipSG nobody's stopping you https://t.co/Nj2znrlW7w\n",
            "10022 @MothershipSG \"I am healthy I have an immune system\" is possibly one of the most insensitive thing I have ever seen written\n",
            "10024 @MothershipSG This is MOTHERSHIT corrupted reporting. Should be POFMAed.\n",
            "https://t.co/o9iCzdOAyH\n",
            "10025 @MothershipSG Abit too late if damage has been done. Vacc and unvacc now have almost similar \"entitlements\".\n",
            "10030 @MothershipSG Dumb ways to die +1\n",
            "10035 @MothershipSG “Nearly all of them were not wearing masks.”\n",
            "\n",
            "Well duh, they aren’t like the majority of sheeps in SG still mindlessly masking up outdoors, inhaling their own breath and accumulating microplastics in their lungs all in the name of health.\n",
            "\n",
            "https://t.co/7lc3efDipp\n"
          ]
        }
      ],
      "source": [
        "for index, row in conversationDF[conversationDF['potential_bias'] == 1].iterrows():\n",
        "    print(row['id'], row['comment'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results Verification"
      ],
      "metadata": {
        "id": "nPIyZH5ILAPh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUZ1IKGdGQEq"
      },
      "source": [
        "## Get the Replies and Retweets of Potentially Bias Users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ChrsZnCtFnr4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59b48d21-7109-4b61-b11b-2adc828d1eae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of potentially bias users: 11 \n",
            "\n",
            "*********** 1531214377 ***********\n",
            "@zheng_corinne @MothershipSG If I suspect that she is where she is from, she wouldn't articulate her distaste in such a manner in her own backyard. Perhaps she would remain muted, hence the hyper aggressive display. Then again, that's no excuse for such ugly behaviour. Shame on her!\n",
            "@MothershipSG See la..govt want us to integrate with foreigners leh...how? only Integrated Bus Interchange maybe la.\n",
            "泼妇。\n",
            "\n",
            "*********** 804973086567055360 ***********\n",
            "For 3 months I've been travelling for a travelogue on SE Asia and its hidden gems. Many stunning places, amazing people and fascinating stories. ASEAN has its problems, but also beauty. Get excited for @ChannelNewsAsia's 5-part doco Tapestry: The Heart of ASEAN 📺 Premiere: 27Feb https://t.co/f6VDbSY7HG\n",
            "My \"studio\" for the weekend covering #ShangriLaDialogue @ShangriLaSG #SLD17 join me @ChannelNewsAsia https://t.co/EuLKjlCs2R\n",
            "\n",
            "*********** 83822931 ***********\n",
            "\n",
            "*********** 3246610436 ***********\n",
            "\n",
            "*********** 1365682034551627779 ***********\n",
            "@MothershipSG Covidiot!😂\n",
            "@MothershipSG The idiots all gathered together for a nice outing. Are they back in the asylum now?\n",
            "@MothershipSG theres barely any checks for vaccination status so this is just leading people to nowhere\n",
            "@MothershipSG wasting time group....yawn...\n",
            "@MothershipSG Uhh the pple protesting do know that as babies we had to take mandatory shots such as Measles Mump Rubella (MMR), Polio etc right? ...right?\n",
            "@MothershipSG Do they have children? Did they ever ask their children if they were willing to be born? Their children’s bodies, their choices!\n",
            "@zacroix @MothershipSG U can protest legally in Singapore if u dk. Like pink dot\n",
            "@MothershipSG \"I am healthy I have an immune system\" is possibly one of the most insensitive thing I have ever seen written\n",
            "\n",
            "*********** 1339037189565415425 ***********\n",
            "\n",
            "*********** 1499140477718872072 ***********\n",
            "\n",
            "*********** 1266607549245091840 ***********\n",
            "\n",
            "*********** 890795094256648192 ***********\n",
            "\n",
            "*********** 1509197192459816962 ***********\n",
            "\n",
            "*********** 1422221555950518275 ***********\n",
            "\n"
          ]
        }
      ],
      "source": [
        "replies_retweets_result_str = \"\"\n",
        "\n",
        "if tweetOption == 0:\n",
        "    print(\"Number of potentially bias users:\", len(conversationDF[conversationDF['potential_bias'] == 1]['user_id'].unique()), '\\n')\n",
        "\n",
        "    for i in conversationDF[conversationDF['potential_bias'] == 1]['user_id']:\n",
        "        allUserTweets = combineTweets([getTweetsLikedByUser(i, header, 100)])\n",
        "\n",
        "        sub_heading = '*********** ' + i + ' ***********'\n",
        "        replies_retweets_result_str += sub_heading + '\\n'\n",
        "        print(sub_heading)\n",
        "\n",
        "        for j in checkForRepliesToNews(allUserTweets):\n",
        "            print(j)\n",
        "            replies_retweets_result_str += j + '\\n'\n",
        "\n",
        "        replies_retweets_result_str += '\\n'\n",
        "        print()\n",
        "\n",
        "elif tweetOption == 1:\n",
        "    print(\"Number of potentially bias users:\", len(conversationDF[conversationDF['potential_bias'] == 1]['id'].unique()), '\\n')\n",
        "\n",
        "    suspectedBiasUsers = ['CardanoAdonis', 'PLafala', 'Erwin_Dawson', 'Coldcappuccino9', '_5andman_', 'Actarus_dEuphor', 'ResenT___', 'mkggoh', 'slowpokemax']\n",
        "\n",
        "    for i in suspectedBiasUsers:\n",
        "        userID = getTwitterUserInfo(i, header)['data'][0]['id']\n",
        "        allUserTweets = combineTweets([getTweetsByUserID(userID, header, 100)])\n",
        "\n",
        "        sub_heading = '*********** ' + i + ' ***********'\n",
        "        replies_retweets_result_str += sub_heading + '\\n'\n",
        "        print(sub_heading)\n",
        "\n",
        "        for j in checkForRepliesToNews(allUserTweets):            \n",
        "            print(j)\n",
        "            replies_retweets_result_str += j + '\\n'\n",
        "\n",
        "        replies_retweets_result_str += '\\n'\n",
        "        print()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(f'{tweets_for_analysis[tweetOption]}/replies_and_retweets.txt', 'w') as f:\n",
        "    f.write(replies_retweets_result_str)"
      ],
      "metadata": {
        "id": "vrpKai7EOgGG"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdqNMLHEGK1B"
      },
      "source": [
        "### Get the Liked Tweets by Potentially Bias Users"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "GgyLOUTyGAjj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2c6be855-3954-41f7-84ff-802cbbe3c775"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "*********** 1531214377 ***********\n",
            "@zheng_corinne @MothershipSG If I suspect that she is where she is from, she wouldn't articulate her distaste in such a manner in her own backyard. Perhaps she would remain muted, hence the hyper aggressive display. Then again, that's no excuse for such ugly behaviour. Shame on her!\n",
            "@MothershipSG See la..govt want us to integrate with foreigners leh...how? only Integrated Bus Interchange maybe la.\n",
            "泼妇。\n",
            "\n",
            "*********** 804973086567055360 ***********\n",
            "For 3 months I've been travelling for a travelogue on SE Asia and its hidden gems. Many stunning places, amazing people and fascinating stories. ASEAN has its problems, but also beauty. Get excited for @ChannelNewsAsia's 5-part doco Tapestry: The Heart of ASEAN 📺 Premiere: 27Feb https://t.co/f6VDbSY7HG\n",
            "My \"studio\" for the weekend covering #ShangriLaDialogue @ShangriLaSG #SLD17 join me @ChannelNewsAsia https://t.co/EuLKjlCs2R\n",
            "\n",
            "*********** 83822931 ***********\n",
            "\n",
            "*********** 3246610436 ***********\n",
            "\n",
            "*********** 1365682034551627779 ***********\n",
            "@MothershipSG Covidiot!😂\n",
            "@MothershipSG The idiots all gathered together for a nice outing. Are they back in the asylum now?\n",
            "@MothershipSG theres barely any checks for vaccination status so this is just leading people to nowhere\n",
            "@MothershipSG wasting time group....yawn...\n",
            "@MothershipSG Uhh the pple protesting do know that as babies we had to take mandatory shots such as Measles Mump Rubella (MMR), Polio etc right? ...right?\n",
            "@MothershipSG Do they have children? Did they ever ask their children if they were willing to be born? Their children’s bodies, their choices!\n",
            "@zacroix @MothershipSG U can protest legally in Singapore if u dk. Like pink dot\n",
            "@MothershipSG \"I am healthy I have an immune system\" is possibly one of the most insensitive thing I have ever seen written\n",
            "\n",
            "*********** 1339037189565415425 ***********\n",
            "\n",
            "*********** 1499140477718872072 ***********\n",
            "\n",
            "*********** 1266607549245091840 ***********\n",
            "\n",
            "*********** 890795094256648192 ***********\n",
            "\n",
            "*********** 1509197192459816962 ***********\n",
            "\n",
            "*********** 1422221555950518275 ***********\n",
            "\n"
          ]
        }
      ],
      "source": [
        "liked_tweets_result_str = \"\"\n",
        "\n",
        "if tweetOption == 0:\n",
        "    for i in conversationDF[conversationDF['potential_bias'] == 1]['user_id']:\n",
        "        allUserTweets = combineTweets([getTweetsLikedByUser(i, header, 100)])\n",
        "\n",
        "        sub_heading = '*********** ' + i + ' ***********'\n",
        "        liked_tweets_result_str += sub_heading + '\\n'\n",
        "        print(sub_heading)\n",
        "\n",
        "        for j in checkForRepliesToNews(allUserTweets):\n",
        "            print(j)\n",
        "            liked_tweets_result_str += j + '\\n'\n",
        "\n",
        "        liked_tweets_result_str += '\\n'\n",
        "        print()\n",
        "\n",
        "elif tweetOption == 1:\n",
        "    for i in suspectedBiasUsers:\n",
        "        userID = getTwitterUserInfo(i, header)['data'][0]['id']\n",
        "        allUserTweets = combineTweets([getTweetsLikedByUser(userID, header, 100)])\n",
        "\n",
        "        sub_heading = '*********** ' + i + ' ***********'\n",
        "        liked_tweets_result_str += sub_heading + '\\n'\n",
        "        print(sub_heading)\n",
        "\n",
        "        for j in checkForRepliesToNews(allUserTweets):\n",
        "            print(j)\n",
        "            liked_tweets_result_str += j + '\\n'\n",
        "\n",
        "        liked_tweets_result_str += '\\n'\n",
        "        print()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(f'{tweets_for_analysis[tweetOption]}/liked_tweets.txt', 'w') as f:\n",
        "    f.write(liked_tweets_result_str)"
      ],
      "metadata": {
        "id": "ieFbeppxRUpG"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Results Visualisation"
      ],
      "metadata": {
        "id": "Q1qbfjwXLP4g"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLxtHV41nSjc"
      },
      "source": [
        "## Visualise through graph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "2-6k1vPtI5-z"
      },
      "outputs": [],
      "source": [
        "# G = createNetworkGraph(conversationTree, head_thread)\n",
        "polarity_map, subjectivity_map, pb_map = getColourNodes(conversationDF)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "MPe8jLGEfM84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "e9391450-d1c8-4852-cbf6-3203b373ba25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Polarity Scores\n",
            "Legend: Green - Positive polarity, Pink - Negative polarity, Yellow - Neutral or Unknown\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<html>\n",
              "<head>\n",
              "<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/vis-network@latest/styles/vis-network.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm/vis-network@latest/dist/vis-network.min.js\"> </script>\n",
              "<center>\n",
              "<h1></h1>\n",
              "</center>\n",
              "\n",
              "<!-- <link rel=\"stylesheet\" href=\"../node_modules/vis/dist/vis.min.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"../node_modules/vis/dist/vis.js\"> </script>-->\n",
              "\n",
              "<style type=\"text/css\">\n",
              "\n",
              "        #mynetwork {\n",
              "            width: 500px;\n",
              "            height: 500px;\n",
              "            background-color: #ffffff;\n",
              "            border: 1px solid lightgray;\n",
              "            position: relative;\n",
              "            float: left;\n",
              "        }\n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "</style>\n",
              "\n",
              "</head>\n",
              "\n",
              "<body>\n",
              "<div id = \"mynetwork\"></div>\n",
              "\n",
              "\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "    // initialize global variables.\n",
              "    var edges;\n",
              "    var nodes;\n",
              "    var network; \n",
              "    var container;\n",
              "    var options, data;\n",
              "\n",
              "    \n",
              "    // This method is responsible for drawing the graph, returns the drawn network\n",
              "    function drawGraph() {\n",
              "        var container = document.getElementById('mynetwork');\n",
              "        \n",
              "        \n",
              "\n",
              "        // parsing and collecting nodes and edges from the python\n",
              "        nodes = new vis.DataSet([{\"id\": \"1619325942\", \"label\": \"1619325942\", \"shape\": \"dot\"}, {\"color\": \"#FFFF00\", \"id\": \"10038\", \"label\": \"10038\", \"shape\": \"dot\", \"title\": \"Score: [-0.5, 0.6681]\"}, {\"color\": \"#FFFF00\", \"id\": \"10000\", \"label\": \"10000\", \"shape\": \"dot\", \"title\": \"Score: [0.125, -0.4364]\"}, {\"color\": \"#FFFF00\", \"id\": \"10021\", \"label\": \"10021\", \"shape\": \"dot\", \"title\": \"Score: [0.05, 0.2263]\"}, {\"color\": \"#FFFF00\", \"id\": \"10001\", \"label\": \"10001\", \"shape\": \"dot\", \"title\": \"Score: [0.5, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10002\", \"label\": \"10002\", \"shape\": \"dot\", \"title\": \"Score: [0.0, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10003\", \"label\": \"10003\", \"shape\": \"dot\", \"title\": \"Score: [0.0, -0.25]\"}, {\"color\": \"#FFFF00\", \"id\": \"10004\", \"label\": \"10004\", \"shape\": \"dot\", \"title\": \"Score: [0.0, -0.25]\"}, {\"color\": \"#00FF80\", \"id\": \"10008\", \"label\": \"10008\", \"shape\": \"dot\", \"title\": \"Score: [0.4, 0.1027]\"}, {\"color\": \"#FFFF00\", \"id\": \"10009\", \"label\": \"10009\", \"shape\": \"dot\", \"title\": \"Score: [-0.1, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10010\", \"label\": \"10010\", \"shape\": \"dot\", \"title\": \"Score: [0.39285714285714285, -0.9509]\"}, {\"color\": \"#FF9999\", \"id\": \"10011\", \"label\": \"10011\", \"shape\": \"dot\", \"title\": \"Score: [-0.2375, -0.6501]\"}, {\"color\": \"#00FF80\", \"id\": \"10017\", \"label\": \"10017\", \"shape\": \"dot\", \"title\": \"Score: [0.5, 0.2732]\"}, {\"color\": \"#FF9999\", \"id\": \"10019\", \"label\": \"10019\", \"shape\": \"dot\", \"title\": \"Score: [-0.5, -0.4939]\"}, {\"color\": \"#FFFF00\", \"id\": \"10020\", \"label\": \"10020\", \"shape\": \"dot\", \"title\": \"Score: [0.0, -0.1531]\"}, {\"color\": \"#00FF80\", \"id\": \"10022\", \"label\": \"10022\", \"shape\": \"dot\", \"title\": \"Score: [0.3333333333333333, 0.4033]\"}, {\"color\": \"#FFFF00\", \"id\": \"10023\", \"label\": \"10023\", \"shape\": \"dot\", \"title\": \"Score: [0.0, -0.4019]\"}, {\"color\": \"#FFFF00\", \"id\": \"10024\", \"label\": \"10024\", \"shape\": \"dot\", \"title\": \"Score: [0.0, 0.0]\"}, {\"color\": \"#FF9999\", \"id\": \"10025\", \"label\": \"10025\", \"shape\": \"dot\", \"title\": \"Score: [-0.15, -0.4939]\"}, {\"color\": \"#FFFF00\", \"id\": \"10026\", \"label\": \"10026\", \"shape\": \"dot\", \"title\": \"Score: [0.0, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10028\", \"label\": \"10028\", \"shape\": \"dot\", \"title\": \"Score: [0.0, 0.0]\"}, {\"color\": \"#FF9999\", \"id\": \"10030\", \"label\": \"10030\", \"shape\": \"dot\", \"title\": \"Score: [-0.375, -0.802]\"}, {\"color\": \"#FFFF00\", \"id\": \"10031\", \"label\": \"10031\", \"shape\": \"dot\", \"title\": \"Score: [-0.0666666666666667, 0.4215]\"}, {\"color\": \"#FFFF00\", \"id\": \"10033\", \"label\": \"10033\", \"shape\": \"dot\", \"title\": \"Score: [0.19047619047619047, -0.3612]\"}, {\"color\": \"#FFFF00\", \"id\": \"10035\", \"label\": \"10035\", \"shape\": \"dot\", \"title\": \"Score: [0.04999999999999999, 0.1744]\"}, {\"color\": \"#FFFF00\", \"id\": \"10036\", \"label\": \"10036\", \"shape\": \"dot\", \"title\": \"Score: [0.25, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10037\", \"label\": \"10037\", \"shape\": \"dot\", \"title\": \"Score: [0.0, -0.3612]\"}, {\"color\": \"#FFFF00\", \"id\": \"10039\", \"label\": \"10039\", \"shape\": \"dot\", \"title\": \"Score: [0.0, 0.0]\"}, {\"color\": \"#FFFF00\", \"id\": \"10034\", \"label\": \"10034\", \"shape\": \"dot\", \"title\": \"Score: [-0.35555555555555557, 0.5423]\"}, {\"color\": \"#FFFF00\", \"id\": \"10040\", \"label\": \"10040\", \"shape\": \"dot\", \"title\": \"Score: [0.05, 0.0]\"}, {\"color\": \"#00FF80\", \"id\": \"10016\", \"label\": \"10016\", \"shape\": \"dot\", \"title\": \"Score: [0.4375, 0.8074]\"}]);\n",
              "        edges = new vis.DataSet([{\"from\": \"1619325942\", \"to\": \"10038\"}, {\"from\": \"10038\", \"to\": \"10000\"}, {\"from\": \"10038\", \"to\": \"10021\"}, {\"from\": \"1619325942\", \"to\": \"10001\"}, {\"from\": \"1619325942\", \"to\": \"10002\"}, {\"from\": \"1619325942\", \"to\": \"10003\"}, {\"from\": \"1619325942\", \"to\": \"10004\"}, {\"from\": \"1619325942\", \"to\": \"10008\"}, {\"from\": \"1619325942\", \"to\": \"10009\"}, {\"from\": \"1619325942\", \"to\": \"10010\"}, {\"from\": \"1619325942\", \"to\": \"10011\"}, {\"from\": \"1619325942\", \"to\": \"10017\"}, {\"from\": \"1619325942\", \"to\": \"10019\"}, {\"from\": \"1619325942\", \"to\": \"10020\"}, {\"from\": \"1619325942\", \"to\": \"10022\"}, {\"from\": \"1619325942\", \"to\": \"10023\"}, {\"from\": \"1619325942\", \"to\": \"10024\"}, {\"from\": \"1619325942\", \"to\": \"10025\"}, {\"from\": \"1619325942\", \"to\": \"10026\"}, {\"from\": \"1619325942\", \"to\": \"10028\"}, {\"from\": \"1619325942\", \"to\": \"10030\"}, {\"from\": \"1619325942\", \"to\": \"10031\"}, {\"from\": \"1619325942\", \"to\": \"10033\"}, {\"from\": \"1619325942\", \"to\": \"10035\"}, {\"from\": \"1619325942\", \"to\": \"10036\"}, {\"from\": \"1619325942\", \"to\": \"10037\"}, {\"from\": \"1619325942\", \"to\": \"10039\"}, {\"from\": \"10039\", \"to\": \"10034\"}, {\"from\": \"1619325942\", \"to\": \"10040\"}, {\"from\": \"10040\", \"to\": \"10016\"}]);\n",
              "\n",
              "        // adding nodes and edges to the graph\n",
              "        data = {nodes: nodes, edges: edges};\n",
              "\n",
              "        var options = {\n",
              "    \"configure\": {\n",
              "        \"enabled\": false\n",
              "    },\n",
              "    \"edges\": {\n",
              "        \"color\": {\n",
              "            \"inherit\": true\n",
              "        },\n",
              "        \"smooth\": {\n",
              "            \"enabled\": true,\n",
              "            \"type\": \"dynamic\"\n",
              "        }\n",
              "    },\n",
              "    \"interaction\": {\n",
              "        \"dragNodes\": true,\n",
              "        \"hideEdgesOnDrag\": false,\n",
              "        \"hideNodesOnDrag\": false\n",
              "    },\n",
              "    \"physics\": {\n",
              "        \"enabled\": true,\n",
              "        \"stabilization\": {\n",
              "            \"enabled\": true,\n",
              "            \"fit\": true,\n",
              "            \"iterations\": 1000,\n",
              "            \"onlyDynamicEdges\": false,\n",
              "            \"updateInterval\": 50\n",
              "        }\n",
              "    }\n",
              "};\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        network = new vis.Network(container, data, options);\n",
              "\t \n",
              "        \n",
              "\n",
              "\n",
              "        \n",
              "\n",
              "        return network;\n",
              "\n",
              "    }\n",
              "\n",
              "    drawGraph();\n",
              "\n",
              "</script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "net = createInterativeNetworkGraph(conversationTree, head_thread, polarity_map, overall_polarity_scores)\n",
        "net.show(f'{tweets_for_analysis[tweetOption]}/polarity_scores_graph.html')\n",
        "print('Polarity Scores')\n",
        "print('Legend: Green - Positive polarity,', 'Pink - Negative polarity,', 'Yellow - Neutral or Unknown')\n",
        "\n",
        "display(HTML(f'{tweets_for_analysis[tweetOption]}/polarity_scores_graph.html'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "NUbyaHAxsUfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "a28400c6-4883-417f-9bf6-479478d5159f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Subjectivity Scores\n",
            "Legend: Light Grey - Subjective comment, Light Blue - Objective comment, Orange - Unknown\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<html>\n",
              "<head>\n",
              "<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/vis-network@latest/styles/vis-network.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm/vis-network@latest/dist/vis-network.min.js\"> </script>\n",
              "<center>\n",
              "<h1></h1>\n",
              "</center>\n",
              "\n",
              "<!-- <link rel=\"stylesheet\" href=\"../node_modules/vis/dist/vis.min.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"../node_modules/vis/dist/vis.js\"> </script>-->\n",
              "\n",
              "<style type=\"text/css\">\n",
              "\n",
              "        #mynetwork {\n",
              "            width: 500px;\n",
              "            height: 500px;\n",
              "            background-color: #ffffff;\n",
              "            border: 1px solid lightgray;\n",
              "            position: relative;\n",
              "            float: left;\n",
              "        }\n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "</style>\n",
              "\n",
              "</head>\n",
              "\n",
              "<body>\n",
              "<div id = \"mynetwork\"></div>\n",
              "\n",
              "\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "    // initialize global variables.\n",
              "    var edges;\n",
              "    var nodes;\n",
              "    var network; \n",
              "    var container;\n",
              "    var options, data;\n",
              "\n",
              "    \n",
              "    // This method is responsible for drawing the graph, returns the drawn network\n",
              "    function drawGraph() {\n",
              "        var container = document.getElementById('mynetwork');\n",
              "        \n",
              "        \n",
              "\n",
              "        // parsing and collecting nodes and edges from the python\n",
              "        nodes = new vis.DataSet([{\"id\": \"1619325942\", \"label\": \"1619325942\", \"shape\": \"dot\"}, {\"color\": \"#ADD8E6\", \"id\": \"10038\", \"label\": \"10038\", \"shape\": \"dot\", \"title\": \"Score: [0.0008762074867263436, 0.5]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10000\", \"label\": \"10000\", \"shape\": \"dot\", \"title\": \"Score: [0.0037204145919531584, 0.45]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10021\", \"label\": \"10021\", \"shape\": \"dot\", \"title\": \"Score: [0.014829045161604881, 0.25]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10001\", \"label\": \"10001\", \"shape\": \"dot\", \"title\": \"Score: [0.0038576279766857624, 0.5]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10002\", \"label\": \"10002\", \"shape\": \"dot\", \"title\": \"Score: [0.003196283709257841, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10003\", \"label\": \"10003\", \"shape\": \"dot\", \"title\": \"Score: [0.0038458744529634714, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10004\", \"label\": \"10004\", \"shape\": \"dot\", \"title\": \"Score: [0.015578856691718102, 0.0]\"}, {\"color\": \"#FF7F50\", \"id\": \"10008\", \"label\": \"10008\", \"shape\": \"dot\", \"title\": \"Score: [0.026702169328927994, 0.8]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10009\", \"label\": \"10009\", \"shape\": \"dot\", \"title\": \"Score: [0.0026126212906092405, 0.0]\"}, {\"color\": \"#FF7F50\", \"id\": \"10010\", \"label\": \"10010\", \"shape\": \"dot\", \"title\": \"Score: [0.014167138375341892, 0.5178571428571428]\"}, {\"color\": \"#FF7F50\", \"id\": \"10011\", \"label\": \"10011\", \"shape\": \"dot\", \"title\": \"Score: [0.14759843051433563, 0.7999999999999999]\"}, {\"color\": \"#A9A9A9\", \"id\": \"10017\", \"label\": \"10017\", \"shape\": \"dot\", \"title\": \"Score: [0.8024165034294128, 0.9]\"}, {\"color\": \"#FF7F50\", \"id\": \"10019\", \"label\": \"10019\", \"shape\": \"dot\", \"title\": \"Score: [0.01277637854218483, 0.6666666666666666]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10020\", \"label\": \"10020\", \"shape\": \"dot\", \"title\": \"Score: [0.01530168391764164, 0.0]\"}, {\"color\": \"#A9A9A9\", \"id\": \"10022\", \"label\": \"10022\", \"shape\": \"dot\", \"title\": \"Score: [0.9998693466186523, 0.6666666666666666]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10023\", \"label\": \"10023\", \"shape\": \"dot\", \"title\": \"Score: [0.49663829803466797, 0.0]\"}, {\"color\": \"#FF7F50\", \"id\": \"10024\", \"label\": \"10024\", \"shape\": \"dot\", \"title\": \"Score: [0.9995007514953613, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10025\", \"label\": \"10025\", \"shape\": \"dot\", \"title\": \"Score: [0.01387838926166296, 0.5]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10026\", \"label\": \"10026\", \"shape\": \"dot\", \"title\": \"Score: [0.012780721299350262, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10028\", \"label\": \"10028\", \"shape\": \"dot\", \"title\": \"Score: [0.014407360926270485, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10030\", \"label\": \"10030\", \"shape\": \"dot\", \"title\": \"Score: [0.02011781744658947, 0.5]\"}, {\"color\": \"#FF7F50\", \"id\": \"10031\", \"label\": \"10031\", \"shape\": \"dot\", \"title\": \"Score: [0.0006758422241546214, 0.6]\"}, {\"color\": \"#FF7F50\", \"id\": \"10033\", \"label\": \"10033\", \"shape\": \"dot\", \"title\": \"Score: [0.2690626382827759, 0.5238095238095237]\"}, {\"color\": \"#FF7F50\", \"id\": \"10035\", \"label\": \"10035\", \"shape\": \"dot\", \"title\": \"Score: [0.008506560698151588, 0.725]\"}, {\"color\": \"#FF7F50\", \"id\": \"10036\", \"label\": \"10036\", \"shape\": \"dot\", \"title\": \"Score: [0.0013242856366559863, 0.75]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10037\", \"label\": \"10037\", \"shape\": \"dot\", \"title\": \"Score: [0.031468868255615234, 0.0]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10039\", \"label\": \"10039\", \"shape\": \"dot\", \"title\": \"Score: [0.27330949902534485, 0.0]\"}, {\"color\": \"#FF7F50\", \"id\": \"10034\", \"label\": \"10034\", \"shape\": \"dot\", \"title\": \"Score: [0.1531762182712555, 0.9111111111111111]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10040\", \"label\": \"10040\", \"shape\": \"dot\", \"title\": \"Score: [0.012535897083580494, 0.1]\"}, {\"color\": \"#ADD8E6\", \"id\": \"10016\", \"label\": \"10016\", \"shape\": \"dot\", \"title\": \"Score: [0.28336089849472046, 0.4583333333333333]\"}]);\n",
              "        edges = new vis.DataSet([{\"from\": \"1619325942\", \"to\": \"10038\"}, {\"from\": \"10038\", \"to\": \"10000\"}, {\"from\": \"10038\", \"to\": \"10021\"}, {\"from\": \"1619325942\", \"to\": \"10001\"}, {\"from\": \"1619325942\", \"to\": \"10002\"}, {\"from\": \"1619325942\", \"to\": \"10003\"}, {\"from\": \"1619325942\", \"to\": \"10004\"}, {\"from\": \"1619325942\", \"to\": \"10008\"}, {\"from\": \"1619325942\", \"to\": \"10009\"}, {\"from\": \"1619325942\", \"to\": \"10010\"}, {\"from\": \"1619325942\", \"to\": \"10011\"}, {\"from\": \"1619325942\", \"to\": \"10017\"}, {\"from\": \"1619325942\", \"to\": \"10019\"}, {\"from\": \"1619325942\", \"to\": \"10020\"}, {\"from\": \"1619325942\", \"to\": \"10022\"}, {\"from\": \"1619325942\", \"to\": \"10023\"}, {\"from\": \"1619325942\", \"to\": \"10024\"}, {\"from\": \"1619325942\", \"to\": \"10025\"}, {\"from\": \"1619325942\", \"to\": \"10026\"}, {\"from\": \"1619325942\", \"to\": \"10028\"}, {\"from\": \"1619325942\", \"to\": \"10030\"}, {\"from\": \"1619325942\", \"to\": \"10031\"}, {\"from\": \"1619325942\", \"to\": \"10033\"}, {\"from\": \"1619325942\", \"to\": \"10035\"}, {\"from\": \"1619325942\", \"to\": \"10036\"}, {\"from\": \"1619325942\", \"to\": \"10037\"}, {\"from\": \"1619325942\", \"to\": \"10039\"}, {\"from\": \"10039\", \"to\": \"10034\"}, {\"from\": \"1619325942\", \"to\": \"10040\"}, {\"from\": \"10040\", \"to\": \"10016\"}]);\n",
              "\n",
              "        // adding nodes and edges to the graph\n",
              "        data = {nodes: nodes, edges: edges};\n",
              "\n",
              "        var options = {\n",
              "    \"configure\": {\n",
              "        \"enabled\": false\n",
              "    },\n",
              "    \"edges\": {\n",
              "        \"color\": {\n",
              "            \"inherit\": true\n",
              "        },\n",
              "        \"smooth\": {\n",
              "            \"enabled\": true,\n",
              "            \"type\": \"dynamic\"\n",
              "        }\n",
              "    },\n",
              "    \"interaction\": {\n",
              "        \"dragNodes\": true,\n",
              "        \"hideEdgesOnDrag\": false,\n",
              "        \"hideNodesOnDrag\": false\n",
              "    },\n",
              "    \"physics\": {\n",
              "        \"enabled\": true,\n",
              "        \"stabilization\": {\n",
              "            \"enabled\": true,\n",
              "            \"fit\": true,\n",
              "            \"iterations\": 1000,\n",
              "            \"onlyDynamicEdges\": false,\n",
              "            \"updateInterval\": 50\n",
              "        }\n",
              "    }\n",
              "};\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        network = new vis.Network(container, data, options);\n",
              "\t \n",
              "        \n",
              "\n",
              "\n",
              "        \n",
              "\n",
              "        return network;\n",
              "\n",
              "    }\n",
              "\n",
              "    drawGraph();\n",
              "\n",
              "</script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "net = createInterativeNetworkGraph(conversationTree, head_thread, subjectivity_map, overall_subjectivity_scores)\n",
        "net.show(f'{tweets_for_analysis[tweetOption]}/subjectivity_scores_graph.html')\n",
        "print('Subjectivity Scores')\n",
        "print('Legend: Light Grey - Subjective comment,', 'Light Blue - Objective comment,', 'Orange - Unknown')\n",
        "\n",
        "display(HTML(f'{tweets_for_analysis[tweetOption]}/subjectivity_scores_graph.html'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "TXoxsAn9M7mz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "outputId": "b027222d-687f-47d2-85cc-ffec8f52e2e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Legend: Red - Potential Bias, Black - Potential Unbias\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<html>\n",
              "<head>\n",
              "<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/vis-network@latest/styles/vis-network.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"https://cdn.jsdelivr.net/npm/vis-network@latest/dist/vis-network.min.js\"> </script>\n",
              "<center>\n",
              "<h1></h1>\n",
              "</center>\n",
              "\n",
              "<!-- <link rel=\"stylesheet\" href=\"../node_modules/vis/dist/vis.min.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"../node_modules/vis/dist/vis.js\"> </script>-->\n",
              "\n",
              "<style type=\"text/css\">\n",
              "\n",
              "        #mynetwork {\n",
              "            width: 500px;\n",
              "            height: 500px;\n",
              "            background-color: #ffffff;\n",
              "            border: 1px solid lightgray;\n",
              "            position: relative;\n",
              "            float: left;\n",
              "        }\n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "</style>\n",
              "\n",
              "</head>\n",
              "\n",
              "<body>\n",
              "<div id = \"mynetwork\"></div>\n",
              "\n",
              "\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "    // initialize global variables.\n",
              "    var edges;\n",
              "    var nodes;\n",
              "    var network; \n",
              "    var container;\n",
              "    var options, data;\n",
              "\n",
              "    \n",
              "    // This method is responsible for drawing the graph, returns the drawn network\n",
              "    function drawGraph() {\n",
              "        var container = document.getElementById('mynetwork');\n",
              "        \n",
              "        \n",
              "\n",
              "        // parsing and collecting nodes and edges from the python\n",
              "        nodes = new vis.DataSet([{\"id\": \"1619325942\", \"label\": \"1619325942\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10038\", \"label\": \"10038\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10000\", \"label\": \"10000\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10021\", \"label\": \"10021\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10001\", \"label\": \"10001\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10002\", \"label\": \"10002\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10003\", \"label\": \"10003\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10004\", \"label\": \"10004\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10008\", \"label\": \"10008\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10009\", \"label\": \"10009\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10010\", \"label\": \"10010\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10011\", \"label\": \"10011\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10017\", \"label\": \"10017\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10019\", \"label\": \"10019\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10020\", \"label\": \"10020\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10022\", \"label\": \"10022\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10023\", \"label\": \"10023\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10024\", \"label\": \"10024\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10025\", \"label\": \"10025\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10026\", \"label\": \"10026\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10028\", \"label\": \"10028\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10030\", \"label\": \"10030\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10031\", \"label\": \"10031\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10033\", \"label\": \"10033\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10035\", \"label\": \"10035\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10036\", \"label\": \"10036\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10037\", \"label\": \"10037\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10039\", \"label\": \"10039\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10034\", \"label\": \"10034\", \"shape\": \"dot\"}, {\"color\": \"black\", \"id\": \"10040\", \"label\": \"10040\", \"shape\": \"dot\"}, {\"color\": \"red\", \"id\": \"10016\", \"label\": \"10016\", \"shape\": \"dot\"}]);\n",
              "        edges = new vis.DataSet([{\"from\": \"1619325942\", \"to\": \"10038\"}, {\"from\": \"10038\", \"to\": \"10000\"}, {\"from\": \"10038\", \"to\": \"10021\"}, {\"from\": \"1619325942\", \"to\": \"10001\"}, {\"from\": \"1619325942\", \"to\": \"10002\"}, {\"from\": \"1619325942\", \"to\": \"10003\"}, {\"from\": \"1619325942\", \"to\": \"10004\"}, {\"from\": \"1619325942\", \"to\": \"10008\"}, {\"from\": \"1619325942\", \"to\": \"10009\"}, {\"from\": \"1619325942\", \"to\": \"10010\"}, {\"from\": \"1619325942\", \"to\": \"10011\"}, {\"from\": \"1619325942\", \"to\": \"10017\"}, {\"from\": \"1619325942\", \"to\": \"10019\"}, {\"from\": \"1619325942\", \"to\": \"10020\"}, {\"from\": \"1619325942\", \"to\": \"10022\"}, {\"from\": \"1619325942\", \"to\": \"10023\"}, {\"from\": \"1619325942\", \"to\": \"10024\"}, {\"from\": \"1619325942\", \"to\": \"10025\"}, {\"from\": \"1619325942\", \"to\": \"10026\"}, {\"from\": \"1619325942\", \"to\": \"10028\"}, {\"from\": \"1619325942\", \"to\": \"10030\"}, {\"from\": \"1619325942\", \"to\": \"10031\"}, {\"from\": \"1619325942\", \"to\": \"10033\"}, {\"from\": \"1619325942\", \"to\": \"10035\"}, {\"from\": \"1619325942\", \"to\": \"10036\"}, {\"from\": \"1619325942\", \"to\": \"10037\"}, {\"from\": \"1619325942\", \"to\": \"10039\"}, {\"from\": \"10039\", \"to\": \"10034\"}, {\"from\": \"1619325942\", \"to\": \"10040\"}, {\"from\": \"10040\", \"to\": \"10016\"}]);\n",
              "\n",
              "        // adding nodes and edges to the graph\n",
              "        data = {nodes: nodes, edges: edges};\n",
              "\n",
              "        var options = {\n",
              "    \"configure\": {\n",
              "        \"enabled\": false\n",
              "    },\n",
              "    \"edges\": {\n",
              "        \"color\": {\n",
              "            \"inherit\": true\n",
              "        },\n",
              "        \"smooth\": {\n",
              "            \"enabled\": true,\n",
              "            \"type\": \"dynamic\"\n",
              "        }\n",
              "    },\n",
              "    \"interaction\": {\n",
              "        \"dragNodes\": true,\n",
              "        \"hideEdgesOnDrag\": false,\n",
              "        \"hideNodesOnDrag\": false\n",
              "    },\n",
              "    \"physics\": {\n",
              "        \"enabled\": true,\n",
              "        \"stabilization\": {\n",
              "            \"enabled\": true,\n",
              "            \"fit\": true,\n",
              "            \"iterations\": 1000,\n",
              "            \"onlyDynamicEdges\": false,\n",
              "            \"updateInterval\": 50\n",
              "        }\n",
              "    }\n",
              "};\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        network = new vis.Network(container, data, options);\n",
              "\t \n",
              "        \n",
              "\n",
              "\n",
              "        \n",
              "\n",
              "        return network;\n",
              "\n",
              "    }\n",
              "\n",
              "    drawGraph();\n",
              "\n",
              "</script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "net = createInterativeNetworkGraph(conversationTree, head_thread, pb_map, [])\n",
        "net.show(f'{tweets_for_analysis[tweetOption]}/potential_bias_users_graph.html')\n",
        "print('Legend: Red - Potential Bias,', 'Black - Potential Unbias')\n",
        "\n",
        "display(HTML(f'{tweets_for_analysis[tweetOption]}/potential_bias_users_graph.html'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-bKt7EnavgY"
      },
      "source": [
        "## Other additional visualisation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "A2WreX-4osDo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46e68ba8-76aa-42f2-9a6e-bc6dc57f1186"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "overall_polarity\n",
            "NEG         4\n",
            "NEU        13\n",
            "POS         4\n",
            "UNKNOWN     9\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Let's count the number of tweets by sentiments\n",
        "sentiment_counts = conversationDF.groupby(['overall_polarity']).size()\n",
        "print(sentiment_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "2LtfnTDRN8mU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58cb1663-8f75-4459-f0bd-d85586669eaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "topic_cluster\n",
            "0     8\n",
            "1    22\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "topic_cluster_counts = conversationDF.groupby(['topic_cluster']).size()\n",
        "print(topic_cluster_counts)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(12,12), dpi=100)\n",
        "\n",
        "ax1 = fig.add_subplot(211)\n",
        "ax1.title.set_text(\"Results of polarity detection\")\n",
        "ax1.pie(sentiment_counts.values, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=270)\n",
        "\n",
        "ax2 = fig.add_subplot(212)\n",
        "ax2.title.set_text(\"Results of text clustering\")\n",
        "ax2.pie(topic_cluster_counts.values, labels=topic_cluster_counts.index, autopct='%1.1f%%', startangle=270)\n",
        "\n",
        "plt.show()\n",
        "fig.savefig(f'{tweets_for_analysis[tweetOption]}/pie_charts_polarity_and_text_clustering.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 965
        },
        "id": "47Rb-jhs-6T0",
        "outputId": "f3769ec3-ec96-4bf9-dc20-a2d621cd2284"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAAO0CAYAAAA1SziYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXxU1fnH8c8zkz2ELOyLEGRLwAEU2dxQq7bVbtaltdZqq63drNq91mXU1mpt1bq11fZXU1tLra07ta6guCCiiGjCvkMIBBhCttnO7487gSEkIQkzc2Z53q/XvELu3Ln3O5Nhnjn3nnOuGGNQSimlMonLdgCllFIq0bT4KaWUyjha/JRSSmUcLX5KKaUyjhY/pZRSGUeLn1JKqYyjxU8ppVTG0eKnlFIq42jxU0oplXG0+KmEExEjIl7bOdqIyI9EZI2IhERkSYL3HfPXQkQuiWy3PJbb7cH+T47s/2Qb+z9cyfb+VPGhxS/NRH3wtd2CIrJZRB4SkWG283VERI4TEa+IlFjY9xnAr4HXga8C1yQ6QyKIyLdF5BLbOQ4lUe8FETlTC1xmy7IdQMXN9cBaIA+YCVwCnCAiRxljWmwG68BxwA3AQ8DuBO/7VCAMXGqM8Sd43/HyMDAHaI1a9m1gB85rnMwS9V44E/gO4O3gvnwgGMd9qySgxS99/dcY807k338SkR3AT4DPAI/ai5V0BgLN6VD4RKTQGNNojAkBIdt5UlUSfjlUcaCHPTPHa5Gfo6MXikiFiDwmIjtFpEVE3hGRz7RbJ1tEbhCRlZF16kVkgYicHrXOPBGZ136nkcOt6zoLFTn0dHvk17VRh2vLI/efHtnXbhHZKyLLReSWQz1ZEckSketEZLWItIrIOhG5RURyo9YxOIc6C6P2e0kX25wnIstEZKqIvCEizSKyVkS+2cG6A0XkzyKyLfKavS8iF3cj90gRuT/yPJsjr/W/2p+/izq8PTuyfh2wqd19ba/hOmAiMDvqec4TkSMj/766gxzHRe674BB5h4vIEyLSKCJ1InInkNvJujNE5DkR8YlIk4jMF5Hjo+730sV7IbLOl0VkceS12Skic0TkiE72NVdEdkWyLRWRKyP3PYTT6iNqHybqsQed8xORo0XkvyKyJ/I+fElEZrZbp+11P15E7hCR7ZF9Py4iA7p6HVXiacsvc5RHfu5qWyAiE3HOdW0GbgUagfOBJ0TkHGPM45FVvcDPgD8BbwN9gWOBY4AXDjPXf4BxwAXA1TiH5gC2R/I9AyzFOYzbCowBju9gO+39CbgYeAz4LTAj8hwqgbMj61wEfAOYDlwWWfbGIbZbCszFaT3/A+f1+r2I+I0x/wcgIvnAvEjWe3EOP58HPCQiJcaY33Wx/Wk4h/7m4BSzcuBbwDwRmWCMaWq3/v3AduAmoLCTbV4F3APsBX4ZWbbNGLNGRF4HLgTubPeYC4EG4MnOgkae50vACOBuYAvOa3pqB+ueCvwXWAzciHOo+avAyyJyojHmbbp4L0S28XPgZpzX/k/AAOAK4FUROdoYszuy3uk475utwO+AWpy/+6civ/8RGAqcHsnbpcj78DVgD8754QBwOc7fZLYxZmG7h9yD8//sRpy/31U474MvHGpfKoGMMXpLoxvOuT0DfAzoDwwHzgHqgBZgeNS6L+IUltyoZYJTEFdELVsCPHOI/c4D5nWw/CFgXbtlBvBG/f7DyLLydutdFVnev4evweTI4x5st/z2yPJT2uXb283tzos8/vtRy3KA94BtQHZk2ZWR9S6MWi8bp7A2AEVdvBb5Hex3ZmS9izr4O78GuDt5D5RHLVvWyd/nG5F1K9pl3Q48dIjXo+15nhe1rABYGVl+ctR7agXwHCDRzxVYAzzfjffCSJzzcNe0W34UTjG6JvK7O7LNdUBJu3Wj930vYDp5Xu3/Jo/jfPE6MmrZEJxiOL+D1/2Fdvu6I5K9uKf/n/UWv5se9kxfL+J8gG3Eaf00Ap8xxrQdGivD+Yb+KFAkIv1FpD/QD/gfMFb29w7dDUwUkbEJfg5tHR4+KyI9ea+eGfl5R7vlv438POswMgVxWg4AGOdc4R9xzh1Ojdp/LU7LsG29AE7rqA8wu7ONG2Oa2/4tzuHmfsAqnNfimA4e8qBxzvH11qM4X4oujFr2cZwvTn87xGPPxGldPda2wDgt0wfarTcFGAs8AvSLeq8V4rQcT+rG3/fzOKdpHm17fGQbtTjF9pTIekcDo4C7TKQlGJWtx1fuFhE3cAbwhDFmTdS2tkaezwki0rfdwx5ot6/XcIryyJ7uX8WPFr/09R2cwzrn4hym68+Bvf/G4HwjvxmnSEbfboysMzDy83qgBFghIh+IyO0iMinuzwD+idMK/ROwLXJ+5/xufFCOxDmstip6oTGmFqeIHM6H0BZjTGO7ZSsiP8uj9r/SGBNut1511P0dEpF8EblJRDbi/L124PxNSoDiDh6ytgfZDxIpEE8DX4pafCHOofCXD/HwkcCqDorK8na/t31pquLg99plOOcIO3pu7bchOIWu/TYq2f9ebTunvewQ2+uuATit2fbPCZy/pwtof85xQ7vf2041lMYok4oBPeeXvt42kd6eIvIEsAB4RETGG2P2sv+Lz29wWnodWQVgjHlVREYDn8X5FnwZcLWIfNMY86fIugbnw6k9d2+fgDGmWUROwvlWfxbwCZzzJi+LyBndaPH0+Jt+ErgH51zYXcCbgA/necyh4y+rzR0s66m/AueJyHHABzg9gu/voHj3VlvuH+EcQu/I3m5swwCfpOOerId6fCJ19r7s6P+HskSLXwYwxoRE5GfAK8B3cTq3tB3CCRhjXuzGNnYCfwH+IiJ9gFdxOsK0Fb9dwJEdPLQ7raxOi1TkA/ilyO37InINTqeNU3AO7XZkPc6H5Vj2t7YQkUE4Laj13cjUmaESGVIQtWxc5Oe6qP1PEhFXuwJSEXV/Z84FqowxP4jKnRfJfTi6+iLwHE4L6kJgIU5L5+FubHM9cJSISLvW3/h2662O/NzTjfdaZzlX4xSPtcaYFZ2sE72vo+j8/dHVftrbDjRx8HMC5+8Zxjm1oFKMHvbMEMaYeTg9Na8SkTxjTB1OB47LRWRI+/Wju2ZHzjtFb2svTqswukv7aqCi3eMm072emW2F5IAP+Mh5yfbaWg4ddqePmBv5eVW75d+P/Hy2G5k6k4XT0w8AEcmJ/L4dpydj2/4HE9W7T0SycHom7gXmd7H9EAe3EK7gMFrQEY10UkCNMUH291y9BPjAGLO0G9uci9Nr8ty2BSJSgNOJJtpinPfHDyNfnA7QbhhAh+8FnJ6gIeAGETng9RFH23v0XZxDwVdJu1li2j2uMbKsyy8VkaMLz+Ocdy6P2tYgnEPFC4wxe7rahkpO2vLLLLcD/8L5gPsDznnBBcAHIvIgTmtwEDALp5fo5MjjPhJnDN9iYCfOMIdzcXrMtfk/nOLyPxH5M845mG8CH+IMjehKW9H4pYjMwem99zRwfeSw57M4rYyBODOVbIrk7pAx5n0RqQK+Eflwm48znOFinI4LrxwiT1e2AD+JfBCuwClwU4BvRDq1gNPh43KcoQ1TcVqE5+J8EbjKGNPQxfafAS4SER/wEc7f4jSg/jAyg/Maf0tErsX54lJnjIk+p/dX4Hs4LeqfdHObD+IcSfhr5HluxRk6cMBwDGNMWEQuwxnq8KGI/AXnnOKwyP72AJ+Oygnt3gvGmNWR7L8CyiOH8htwOrecjfOa/yayr2/hvH+WRPa1FaeVNhGnM0/0fu4Wkf8BIWPMnE6e57U4588XiMj9OJ2eLsf5Avbjbr5WKtnY7m6qt9je2N/d+tgO7nPhfPCtItI9HudQZRXOB4Qfp7A8DZwT9bif4xwO24XzwVaNMwdmdrvtX4jzDb8Vp/v/GXRjqENk2bWRfYci95fj9EZ9AueDsjXy8xFgbDdehyycjjprIs9rA3ALUcM6Ius9RM+GOizD6dX5Bs75tnXAdzpYdyDOF4LtkexLgUs6WK99t/qSqMc14BySHB/Zz0Pd/Du33VcetWwQTmHdE7lvXgePWxZ5/Yf14P02AmcsYGMk8104BWbfUIeodacA/8bpxNMSeU7/BE491Hsh6r7P4/Se3Bu5VeN8CRvXbhvH47TY9kTWex/4btT9bpzet3U4hy7NId6fR0f+Fg2R5/oyMKs7//eAkzt6PfRm9yaRP45S6hAird/+xpijbGeJBxF5D9hpjPmY7SxKxZue81NKISLH4rTM/mo7i1KJoOf8lMpgInIUzmHcH+Ac+v6n3URKJYa2/JTKbOfiDGHJBi4wekUDlSH0nJ9SSqmMoy0/pZRSGUeLn1JKqYyjxU8ppVTG0eKnlFIq42jxU0oplXG0+CmllMo4WvyUUkplHC1+SimlMo4WP6WUUhlHi59SSqmMo8VPKaVUxtHip5RSKuNo8VNKKZVxtPgppZTKOFr8lFJKZRwtfkoppTKOFj+llFIZR4ufUkqpjKPFTymlVMbR4qeUUirjaPFTSimVcbT4KaWUyjha/JRSSmUcLX5KKaUyjhY/pZRSGUeLn1JKqYyjxU8ppVTG0eKnlFIq42jxU0oplXG0+CmllMo4WvyUUkplHC1+SimlMo4WP6WUUhlHi59SSqmMo8VPKaVUxtHip5RSKuNo8VNKKZVxtPgppZTKOFr8lFJKZRwtfkoppTKOFj+llFIZR4ufUkqpjKPFTymlVMbR4qeUUirjZNkOoFRa8xYXAf0jtwFR/46+leJ8EQ0DJvIz+t/tf7b9OwBsB7YBtZGf+//t9bUk4ikqlYrEGGM7g1KpyVucBYwBJkRuFcAQ9he1fkCutXzgo31BdH5uBj4EluH1NduLp5Q9WvyUOhRvcTYwjv1Fru02DsixmOxwhYFVwAfA0qifa/D69INBpTUtfkpF8xaXAicBU3EK3ESc1l0mnSJoBJbRvih6fTutplIqhrT4qczmLS7GKXanRG6T0I5gnVkHzIvcXsHr22AzjFKHQ4ufyixOB5QTgZNxit3RgNtmpBS2hrZCCC/i9dXajaNU92nxU+nNW1wIHM/+lt1UMusQZqIY4H3gOeC/wBt4fUG7kZTqnBY/lX6cgvdZ4ALgDFK7U0qq2gO8iFMIn8Dr22E5j1IH0OKn0oO3OBf4JE7B+xRQYDeQihLEaRE+DDyl4w9VMtDip1KXM87uY8AXgbOBYruBVDfsAR7DKYTzdUiFskWLn0ot3mIBTsBp4Z2LM2uKSk0bgb8DD+P1fWQ7jMosWvxUavAWVwKXAucDR1hOo2LvXeBvwCN4fdtsh1HpT4ufSl7eYhfO+bvv4RzeVOkvhNNR5o/Ak3h9Yct5VJrS4qeSj7e4BKeV9x1glOU0yp7lwG+Bv+L1tdoOo9KLFj+VNDxVnrFf2NNw4bX1u34IFNrOo5JGLfA74Pd4fT7bYVR60OKnrPNUeWYBPwI+K8bUL1q/sSjXkGc7l0o6DcADwJ14fZtth1GpTYufssZT5TkV8OJMN7bPN3b5Flyx23eClVAqFQRweonerr1EVW9p8VMJ56nynADcjDO/5kFywmb14vUbRyc0lEpFBngWuA2vb4HtMCq1aPFTCeOp8szEKXqnHWrdW+t2LD6rsWlq/FOpNPEG8DO8vldtB1GpQYufijtPledY4Cac6ce6pV8w9O68jZuPiV8qlab+DfwIr2+t7SAquWnxU3HjqfKMBG4HzuvN4+dsrl050e8fG9tUKgO04vQO/QVeX4PtMCo5afFTMeep8hQCPwN+AL3vtVnR6l/wry212vFF9dY24DrgzzpYXrWnxU/FjKfKI8CFwK3AsMPeoDGtL2/cvGdAKKzzd6rD8T5wNV7fK7aDqOThsh1ApQdPlWca8DrObP2HX/gARHJv6VemXdnV4ZoMvIy3+Am8xWNsh1HJQVt+6rB4qjz9gN8AFwMS6+1HBr0X6qB3FSN+4F7gJp0tJrNpy0/1mqfK8wXgI+AS4lD4AIxIvz+UFC+Kx7ZVRsoBvg+sxFt8ge0wyh5t+ake81R5hgC/Bz6biP3lhM3qd9ZvPFLiVGBVRvsn8G28vp22g6jE0paf6hFPlecynNZeQgofgN8lo5/pU7A4UftTGeULwDK8xd0eg6rSg7b8VLd4qjyjgAexdF29slDovfkbNh9tY98qYzwAfB+vr9F2EBV/2vJThxRp7X2AxQvK7nS7j/4gJ2eFrf2rjPAN4H28xcfbDqLiT1t+qlOeKk8xzrfh821nARjX6n/931tq9YNJxVsY5yK61+lFdNOXtvxUhyLX2FtCkhQ+gBU52dPq3O462zlU2nPhXF9yEd7iybbDqPjQlp86gKfK4wKuAW4AsizHOcgpjU3z767bMdt2DpUx/MCNOJdNCtkOo2JHi5/ax1PlGQb8jU6us5cMxJidb6/flJ9nTL7tLCqjvAqci9e33XYQFRt62FMB+66qvoQkLnwARqTs9yXF79jOoTLOSehh0LSixU/hqfJcBfwP6G87S3c8XFw03DhX8VYqkUYCr+MtPtd2EHX49LBnBvNUeXKBP+LMy5lSbt5ev+hzexun2c6hMpIBbga8eH36AZqitOWXoTxVnqE45zFSrvAB3FFWkm07g8pYAlwPPIa3uNB2GNU7Wvw6ISIPiYiJ3PwiskpErheRrMj9bhG5WkQ+EJEWEdklIv8VkePbbcctIj8VkRoRaRaRnSKyUEQus/PM9g1jeAeYbivD4drldk95Pzdnue0cKqN9HngDb3G57SCq57T4de05YAgwFmfQqxf4kYgIMAfn29/vgEqcjiIbgXki8rmobdwAXI1zRekJwCk4A8dLEvIM2vFUeS4B5uE8r5R2Y/+yHbYzqIw3CacjjA6/STF6zq8TIvIQUGKM+VzUsueBIuAunOL3GWPM0+0e929gNjDSGNMoIkuAx40xNyYsfCc8VZ7rgJts54gZYwIvbNxSPzgUGmw7isp4AeAKvL4/2g6iukdbfj3TjHM9sC8BK9oXvojfAv2A0yO/1wKnisiAxEQ8mKfK4/JUee4nnQofgEj2L/qX6qFPlQyygT/gLb4fb3HSTQ6hDqbFrxvEcRrwceBlYBxQ3cnqbcvHRX5+HxgA1IrIUhH5g4gk7PIpnipPHvAv4FuJ2mcivZqfP6lZpMl2DqUivgU8gbc413YQ1TUtfl37lIjsBVqA/+Jc+NIbua9bF1Y1xnwEHAXMBP4PGAg8LSJ/innadjxVnhKc8Xufj/e+bDEipfeV6qB3lVTOAp7CW6yzECUxLX5dewWYgtPhJd8Yc7ExphFYgdPJpSNty/ddfscYEzbGLDLG3GWM+TxwCXCpiIyKV/DIVGWv4cxMkdYe6Vs0Qge9qyRzBvCsDoVIXlr8utZojFlljNlgjAlGLZ8DjBWRT3fwmB8A9cALXWz3o8jPuPzH8FR5RgNv4LQ4015ApPzxPoWLbOdQqp1TgOfwFhfZDqIOpsWvd+YAjwNVInKpiJSLyCQR+SPwGeCySAsREXksMh5whoiMFJGTgftwWoY1sQ4WKXzzgBGx3nYyu7OsRM+xqGR0AvAC3mIrQ5tU57T49YJxxoecD9yCM4ZvOc4hxpHAycaYJ6JW/x/waeBpnIJXhVP0zmjXmjxsUYVveCy3mwp2u92T383N6awTklI2zQBewltcZjuI2k/H+aWJTC58bUb7/W88sbn2ONs5lOrEUuA0vSxSctCWXxrQwudYnZ09bavbvdV2DqU6MQmYh7dYJ2VIAlr8UpwWvigi2Tf3L1tx6BWVsmYCMB9v8TDbQTKdFr8U5qnyjEIL3wEW5OdNbhJptJ1DqS6MwymAGdUpLdlo8UtRnirPIOB5tPAdwIiU3FdavNh2DqUOYTTwX+0Fao8WvxTkqfL0xbnixBjbWZLRP/oWjQhD2HYOpQ5hAvBvvMUJuTZl1GXaftpu+edExET+fXLUpdza3wZHbeeJDrbf9tiUKOha/FJM5OrrT+HMPKM6EBn0rlOeqVRwKvBgAvfXAvxEREoPsd54nMueRd/q4pwtobT4pRBPlUeAh3EumaS6oIPeVQq5GG/xDQna14s4V5r52SHWqzPG1La7pdXRFC1+qeW3wHm2Q6QCn9s9eXFurg56V6nCi7f4ogTsJwRcA1whIhndX0CLX4rwVHmuxJlNRnXTTf3LdtnOoFQP/Alv8cnx3okx5nFgCdDVBbY3icjeqNuH8c6VaFr8UoCnynMmcIftHKlmTXbWtC1ZOuhdpYwc4HG8xZ1dMSaWfgJcLCKd7etEnH4FbbczE5ApobT4JTlPlWcc8Aj6t+o5keyb++mgd5VSSoC5eIsHxXMnxphXceYd/lUnq6yNXNGm7bY+6r49QHEHjynBOayaEuNs9QM1iUWGNDxJx2801Q0L8vOm6KB3lWLKSczFcH+KM+n+rB4+bjkwUUTadyo7BqdoBmIRLt60+CUpT5XHBfwdqLCdJaWJFN+jg95V6pkO/B1vcdw+o40xH+B8xnyvg7sHisjgdre28Yh/x7l49F9FZKqIjBGRrwFX4XTKSwla/JLXTcCnbIdIB3P6Fo3UQe8qBZ0N3BrnfVxPx3VgObC13W0qgDFmN845wWycMcdLcAro94E/xjlvzOgljZKQp8pzHvCo7Rzp5LodOxee37B3hu0cSvXCZ/D6nrYdIt1oyy/JeKo8RwF/sZ0j3fyuNO7nT5SKl4d0EuzY0+KXRDxVngKcFl+h7SzpZo/bPWlRXu5HtnMo1QtlwD8TNQdoptDil1zuAhIxxicj3dSvbLftDEr10kzgFtsh0okWvyThqfKcC3zddo50ti47a/rmLPcW2zmU6qUf4C3WTnAxosUvCXiqPCNI7MzumUkk66Z+ZSttx1CqlwTn/N9Q20HSgRY/yzxVHjfODC4pcQ2sVPdGft6URpG9tnMo1Uv9gL/gLRbbQVKdFj/7rgeOtx0iY4gU/6605F3bMZQ6DGfQ8cB01QM6zs8iT5XnBGAe4LYcJaNkGbNx8bqNw1z65U+lrhZgGl7fMttBUpX+57fEU+XJB/4PLXwJFxQ54tGiPm/bzqHUYcjDmf5ML9rcS1r87LkRGGs7RKa6u7REx1KqVDcJHf7Qa1r8LPBUeY7FmQdPWdLgdnkW5uWm3QU6Vca5Cm/xVNshUpEWvwTzVHmygT+jhzutu7l/2R7bGZQ6TC7g/nhe/SFd6QuWeD/FOVyhLFuflTV9Y1bWJts5lDpM04HLbIdINVr8EshT5akErrWdQ0WIuG/qX7radgylYuAWvMX9bIdIJVr8EiRycdo/Azm2s6j93srLO3qvSIPtHEodpn7Ar2yHSCVa/BLna8As2yFUOyJ97yrTQe8qLVyGt1ivWdlNOsg9ATxVnr7ASmCg7SzqYG5jNi1et3GIWzshqdS3GJiO1xe2HSTZacsvMa5DC1/SCokMn9O3zyLbOZSKganA5bZDpAJt+cWZp8ozFliGnutLan3C4WVvrt90lO0cSsXALmA8Xt9220GSmbb84u8OtPAlvb0u11Fv5uXpPIkqHZQCt9kOkey0+MWRp8pzBqAXn0wRN/cv1UsdqXRxCd5i7WDXBS1+ceKp8mQBd9rOobpvY1bWtA066F2lB8GZ+UU7cXVCi1/8fB2YYDuE6gER9439y3TQu0oXU4ALbIdIVtrhJQ48VZ48YBUwzHYW1UPGNLyxfpMpMqav7ShKxUANMFGHPhxMW37xcTla+FKTSNGdZSXv2Y6hVIxUAOfbDpGMtOUXY54qTwGwBhhkO4vqHbcxmxev2zhYB72rNPEh4MHr0w/7KNryi71vo4UvpYVEhj3St0iv9K7SxUTgHNshko22/GLIU+XpA6wF+tvOog5PYTj84VvrN020nUOpGHkfOFpbf/tpyy+2rkALX1podLkmvp6f94HtHErFyGTgs7ZDJBNt+cVIZPLqtUCZ7Szx0Li8kR1zd9C8vpng7iAjrhhB36n7O0Rue3wbvoU+AjsDSJaQX57PoHMGUTC6oNNt1r9cz86XdxLYEQAgd1guAz87kKJJRfvW2fqPrexesBvJFQafO5iS40r23ed728fu13cz8uqRcXjGcEQg8NbcTVtnxmXjSiXeu3h9U22HSBba8oudb5KmhQ8g3Bomb0QeQy8a2uH9uYNzGXrRUMb+YixH/vxIcvrnsO436wjuCXa6zezSbAafN5jR3tGM9o6mT2UfNvxuAy2bWwDY894efG/6KP9hOYPPH8zmv2wm2OBsL9QUYtu/tzHkK0Ni/2QjNmZlTVuflbUxbjtQKrGOwVusM05FaPGLgchsLlfYzhFPRZOKGHTOoANae9FKZpXQZ2IfcgbmkDcsj8EXDCbcHKZlU0un2+x7dF+KJheROziX3MG5DDp3EK48F02rmgBo3dpKYUUh+aPyKZlZgivfhX+7H4DaR2spO7WMnH5xnDbVGfS+Nn47UCrhrrMdIFlo8YuN84HhtkMki3AwzK55u3Dlu8g7Iq9bjzFhw+63dhNuDVMwxjlUmndEHs3rmgk1hmhe14zxG3IH5dK4opGW9S30O71fPJ8GAIvyco/e4xJf3HekVGJMx1v8CdshkkGW7QBp4vu2AySDPUv2sOn3mwj7w2QVZ1H+o3Kyirp+i7VsbGHNL9YQDoRx5boYccUI8oY5BbPIU0TTrCZW37gayRGGf304kits+esWhl82nJ0v76T+xXqy+mQx9KtD9z0upkSK7igtne+t3zk79htXyorrgedsh7BNO7wcJk+V5yRgvu0cibTskmUHdXgB57xgYHeAUEOInfN30ljdyOjrR5PVt/MCGA6GCdQHCDeH8S3ysevVXYz66ahOC1ndE3WEmkKUnljKutvXMeYXY2h4v4H6F+sZc+OYmD7PNm5jNr+zbuOgLP2yqNLHKXh982yHsEkPex4+bfVFuHJd5A7KpWBMAcMvHY64hV2v7ur6MVnOY/LL8xl83mDyjsij/oX6Dtdt3dLK7jd3M/DzA2msaaRgfAFZfbMonl5My/oWQs2heDyttkHveqV3lU4y/mrvWvwOg6fKMwb4tO0cycqEDeFAD+fTNWACBx+NMMawuWozg784GHeeGxM2mJCznglG1o/j1L33lxYXx2/rSiXc2XiL43/SPHmtDLoAACAASURBVIlp8Ts8V5Ihr2GoJUTz+maa1zcD4N/hp3l9M/56P+HWMLWP1dK0qslZvq6ZTX/eRHBXkOLp+2vG2tvWUv/i/lZd7b9qaVzeiH+7n5aNLc7vNY2UzCo5aP+75u8iqyiLvkc7h1oLxhbQWN1I06omdjy/g9yhubgL4zcVZ6PLNeE1HfSeMLcuaEVu3MNVz+3vLXz5082MvruB/F/uYcDtDXx2ThM1O7pu7XvntVBx714Kb9lD6W17OO2vjSzctH/4TWvQcNHjzfT91R7G3bOXF9ccODTn9tdbuWJuc2yfXHLIBS6yHcImPYfRS5EJrL9iO0eiNK9tZt1t6/b9XvuPWgBKji9h6MVD8W/1s2HBBkJ7Q7j7uMkflc+oaw48d+ev8+8bpwcQ3BNk0wObCPqC+3qGlv+gnD5H9Tlg30FfkO1Pb+fIa4/ct6zgyAL6f6I/6+9cT1bfLIZ9Pf4X0fhlv7Km5zZtift+Mt2izSH+uNjPpEEHfq+cOtTNhZOyGVHsYmezwTuvlTMebmLtlX1wu6TDbY3r5+beM7M4stRFc8Bw51t+zvhbE6uu6MOAQhcPLA6weEuINy8t5L+rgnzp381s+2EfRIS1u8I8+G6Ad75RmIinbcOlwF22Q9iiHV56yVPl+QpQZTuHSiBjwk9v2rqpPBgcYTtKutrrNxzzx0buPyuPX7zaypTBbu76RMedn5ZuCzH5D42suqIPo8u6dwBmT6uh+NYGXryogI8dmcW3n22mb65w62l5NAcMBbc0UPdDpzB+4m+NXD41h7Mrs2P5FJPNLLy+t2yHsCEjDtnFyddsB1AJJuLy9i9bZztGOvvO3BbOGpvFaUd2fVCq0W/4y3sBRpUIRxR33Oprzx8yPLDYT3EuTB7sfPRNHuRmwYYQzQHD/1YHGdJH6F8g/H1pgLwsSffCB3CZ7QC26GHPXvBUeUYDJ9nOoRJvcV7uMT6X+IrDRjvAxNicZQHe3Rpi0dc7P8x4/yI/P36hhcYAjO/n4oWLCslxd138nlkR4IuPNdMUgCFFwgsXFdK/wCl+Xzs6m6XbQky4fy/9C4RHz8tnVwtcP6+FeRcXcu3LLcxZFmB0mYv/+0w+w/qmXXvhC3iLr8Lr22s7SKKl3V8yQb4GdO/rpkovIn1+W1a6xHaMdLPRF+bK51r4++fzycvq/L/WhZ5s3ru8kPmXFDCun4vzH2uiJdj1qZtTyrNY8s0+vHFpAZ8YncX5jzVR1+h0Dc52C/edlc/aK4tY9PU+nDAiix8838L3pufwXm2IJ2qCvP/NPswc5uZ7z3U+VV8K6wN80XYIG/ScXw95qjwuYAMQ/x4WKim5jNm6eN3GATroPXaeqAlw9j+biW7EhYzzDdMl0Hpt0UGdWvwhQ+ltDfzp0/lc4On+4cmx9+zla1Oy+dmJuQfd98raID95sYU3Ly3kRy+0kuWCX5+ex4d1IU56qIn6Hxd1sMWUtxCvL+OuXqL/eXvu42jhy2hhkSEPFxe98VVfw3G2s6SLj43K4oNvHXi486tPNlPR381Pjs/psDenMc6tNdSzL/BhYzp8TEvQ8J25TuvT7RJCYWf7AIEwhMJp21CYgbfYg9eXUUN59LBnz2lHF8UfSopLbWdIJ0W5wlED3QfcCrOFfvnO8jW7wvzqtVYWbwmxwRfmjY1BzvtXM/nZwplj93+Hr7h3L49XO9eHbPQbrnmphbc2BVm/O8ziLSG+9mQzm/cYzptwcEvx5vmtnDk2i6OHOONFjx/h5j81AZZuC3Hv236OH5HWbYWM6/iS1n/NWItcsFZndFE0uVyV8/Pzls5ubplkO0smyMuC1zaEuGuhn13NhkF9hJNGunnjawUMLNz/HX55fRhfq9NCc7ugZkeYqveb2dFk6JcvTBvm5rWvFjJx4IETIiyrC/HoR0GWXL6/9XnuhCzmrcvixL80Mr6fi0fO6fzCzGngy3iLf4zX12o7SKLoOb8e8FR5vgw8bDuHSg5DA8G3/7dpy3TbOZSKkfPx+v5lO0Si6GHPnjnfdgCVPLZkuY9dk5213nYOpWLks7YDJJIWv26KHPI8w3YOlUScQe9a/FS6+ATe4vhNkJtk9JxfN835VfDM5lxWvD5Bdj49wzV6W6noldsV7+XmTvW5XLuLw+GDZ+NWKrX0A2YBC2wHSQQtft3kgnMKW/Gc8Z7hjPdCNOfw0cLxUvfUDFf5pgFSbjufskSk8Paykvm/2KFXeldp4SwypPhph5duqK6ozAO248yGcJDWLFYuHiObn5rpGr5miMTncuIqabmM2frOuo39syHtJ4JUaW8ZXp/HdohE0OLXDdUVlZ8Cnu7OugE3a5ccKeufmuEatPwIqYxzNJUkrtq5+41LfXt00LtKB+V4fWl/LlsPe3ZPt3tBZYcYNW2lGTVtZYigi40fjpA1T82Ufh+Uy0REdD7QNPXHkr6ll/r22I6hVCx8CrjPdoh40+LXPb3q5ZkV5ojJ68wRk9cZQsLW5cNZ8fQMV/G7Y2SSEdGetmmk2eWqfKUg//1Tmpon286i1GHKiOKnhz0PobqicjSwKpbbDAvbVw+h+pnprsKF42Vy2CX6JSQNDA4G335how56VymvBeiH19dkO0g86YfuoX0s1ht0GQaM3cKAq58IY2DX+oF8OHeaK2fBBJkczJKDp5pXKaHW7Z62Ojtr3ehAsNx2FqUOQx5wGvCU7SDxpC2/Q6iuqPwnCZrZxcCezf344L/HutzzPTLJny1pPZlgOprc0vrq37Zu0wsdq1T3IF7fN2yHiCctfl2orqgUoA7on+h9G2jaVsLS549xhV+aIp7mXEnLC4mlHWOaXtuwubUkHNarPqhUtgWvL60v3abFrwvVFZWTAetX7TbQWt+X91+c4vI/f4wctTdfdDaRJPbphsb5t+yo10HvKtUdg9f3nu0Q8aLFrwvVFZVXA3fYzhHNQGB3IUvnTZKmuce6Knx9ZIDtTOpALmO2LVq3sTQHcmxnUeowXInXd7ftEPGiHV66FvPOLodLILu0kalnv2n43JuhUEM+S147SnzPTHeNr+8rg23nUxAWGVRV3Pf1r/v2HG87i1KHYZrtAPGkLb9OVFdUZgE7gZQ412bANOXy4esTpF4n3rYvLxxevmj9pvG2cyh1GJbj9VXYDhEvWvw6UV1ReQyw2HaO3mrOpvrt8bLtyZmukZsGyCjbeTLRndu2LzmtqXmK7RxK9ZIBSvCm59RFetizc1NtBzgc+QEqZy8zlbOXhaIn3h62ZoiMtZ0tU9zarzRwWlOz7RhK9ZbgfA6+YjtIPGjx61xKF79ouUHGHldjxh5XEyLgZu37o2T9UzNdg2p04u242uZ2H7siO3vtuEBAW94qVU1Di1/GSZviFy07xKhjV5lRx64KEXSx6cMRsvqpmVL2QbkcpRNvx5iIePuXbXpk6zYtfipVpW2nFz3n14HqispsoAHImKnGQsLWFcNY8fQMV8niseLRibdjxJjmVzdsbi4Nh8tsR1GqF9bj9ZXbDhEPWvw6UF1ROQVI28GdhxIWtq8ZTM0z010Fb1XoxNuH66y9jfNu3V5/su0cSvXSALy+HbZDxJoWvw5UV1ReCvzJdo5kYGB3ZOLtbJ14u3d00LtKcWfi9f3XdohY00NbHUvL8329IVBSXsfx3342PP3vt4f8dz4QfOOMxeG3cgImrS93EkthkUF/Ke67yHYOpXopLc/7acuvA9UVlQsBvS5bFww01ZWw9H868Xa36KB3lcKewev7tO0QsabFr53qiko3sBfnmlaqGwy01hfx/ktTXP7/TdWJtztzx7bt753e1Hy07RxK9VAtXt8Q2yFiTYtfO9UVleXAWts5UpWBoK+Qpa9MkkadePtAA4PBRS9t3JKWh5BU2huG17fFdohY0l58BxtjO0AqE8gqaeSYtom39+az5NWJsueZGa5xmT7xdp3bfezy7Ow14wOBI21nUaqHRgFa/NLcaNsB0oWAu6iZKWe9YzjznZBpyuWDNypl51MzXKO3lWXgxNvOoPfN/9i6TYufSjUjgddth4glLX4H0+IXBwJS2Irn9CWG05eEaMmmeuF4qXtypmtEJk28vSw359idLld9WTjcz3YWpXpghO0AsabF72B62DMB8tpNvP1uZOLt1ek+8bZI/q39St/+9Xa90rtKKSNtB4g1LX4H05ZfguUGGTurxoydVRMi4GLd0iNl/ZMzXQPTdeLt5woLJvxie31rTgZNn6dSnrb8MoAWP4uyw5RPXWXKp0Ym3v5ohKx+aob0WzpKJqbLxNtGZMCfS/ou+NbuPSfYzqJUN6Vd8dOhDlGqKyoHA1tt51AHi5p4u/jdMeIJu8RtO9PhyA2HV76zflN6H+JV6aQBr6+v7RCxpMUvSnVF5QnAa7ZzqK6FYceaIVQ/M91VsHC8TAq5Jdt2pt74Td2Odz/e2HSM7RxKdVMpXt9u2yFiRQ97HugI2wHUobmg/5itnHjVk2EM7N4wkGXPHuvKWTAxtSbevq2sJPzxRp0iVaWMkYAWvzSls5GkGIGSkXWc8O25Yb41l4YtZSx+7liX65VJMsmfLQW283Vlu9s9tTone3WlP6DnmVUqGAG8bztErGjxO1B/2wFU7wkUDdvJcZc+H+Zrz9NUV8Jbzx/tCr04RTzNeZJ85ytE5Mb+ZVvmbNmmxU+lgrQa7qDF70Da8ksTAgWDdjPzolfCfPkVWncWseilKa7W546RiXsLpNR2vjYf5uRMq3e5dvQLh/WLl0p2adXjU4vfgfQDKA0J5PZrYNr5r4U57zWCvkIWz/NI47PTXJXWJ94WyftVv9KFv9FB7yr5afFLY1r80lxk4u2pn3vL8Nm3QuG9+Sx5baL4np7uGldfLFYu2/J8YcEEvw56V8lvmO0AsaTF70Ba/DKIgKuomSlnvmP4pDPx9rI3K6T+qZmuI2vLJGE9f43IgAdLihd8Z7dPB72rZJbUHch6Ssf5RamuqNwKZPRld5SjJZvqt8fJtidnukZuHBj/ibdzw+FV76zfpPPKqmRWjdc3wXaIWNGW34F0pn0FOBNvn/ShqTzpwxD+LFa9O1o2PTnTNWz10PhMvN3qco2ZW1iw+MzGpqnx2L5SMZBvO0Asacsvorqisi/gs51DJbeAi3VLR8m6p2a6BlaPkJh+C+4fDC1+ZeNmLX4qWdXi9Vk5Lx4P2vLbr9B2AJX8ssOUT11tyqeuDhFysemjI2T1kzOlbOkoOepwJ97ekeWe+lFO9qoJ/oAe/lTJKK1aflr89kvJ+SGVPe4wwz3rzXDPekNIqF05jBVPT3cVLR4rk3o78ba3f7/aR7fUavFTySjPdoBY0sOeEdUVlaOBVbZzqNTXNvH2s9Nc+W9VyOQeTbxtTOsrGzfv6R8K64QLKhm58PrSomho8YuorqisAKpt51DpxcDuDQNYNneaK2fBRJkUyJJDfns+vbFp/h11O3TQu0pGBXh9zbZDxIIWv4jqikoPsNR2DpW+DDRsKeOD56a6ZN4kmdSaIx2eZxZjdixav7FPrkmvw0wqLfTD69tpO0QsaPGLqK6oPAZYbDuHygwGmuuKef+FY1yhFzqYePvru32vfW+X70Rb+ZTqxDC8vi22Q8SCFr+I6orKGcBbtnOozGPAv7OI91+a7Gp9bqoz8XZO2Kx+Z/3GIwUOqwepUjE2Bq9vte0QsaC9PffT3p7KCoGcfg1MO39BmPMWEPQV8O78SdK4cERe/UxXy3Tb+ZSKkjaH4l22AyQRLX7KOoGskiaO+exb5sQVb8zy7TV5H9nOpFSUtBnrp8Vvv16Ny1IqXna7inKmt94/ss6UvGM7i1IRObYDxIoWv/1abAdQqr0m8gqPa7178vLw8NdtZ1GKNPqc1OK3X5PtAEp1JEhW9sf9tx03PzRpnu0sKuOlxRg/0OIXTYufSmIiFwd+evLDwdPmG4N20Va2pM3npBa//dLmj6rS13XBr83+RfDLbxqD33YWlZHS5nNSi99+afNHVentz6Ezj/tO4MplxtBgO4vKOGnzOanFb7+0+aOq9Dc3POOYc/zezSEj221nURklbT4ntfjt1wx6LkWljnfNuIrT/Lc3+417ve0sKiO0pMsVHUCL3z6VNdWGNOrGqzLDWjN0xKzWewt1MLxKgN22A8SSFr8DpU2TXmWOeor7z2i9b4QOhldxtst2gFjS4ncgn+0ASvVGI/l9dDC8irO0uJRRGy1+B9pqO4BSvdU2GP7VkGe+7SwqLWnLL41p8VMpTuQrgZ/N/lvwYzoYXsWaFr80psVPpYVrg5fOviV4oQ6GV7GkxS+NafFTaePB0Fk6GF7Fkp7zS2Na/FRamRueccx5/hs26WB4FQPrbAeIJS1+B9piO4BSsfaOGV+pg+FVDKyyHSCWtPgdSFt+Ki2tNUNHHNd6T0Gjyau2nUWlrNW2A8SSFr8DafFTaWsHJQOmt953RJ0pXmw7i0o5e/H6am2HiCUtfgeqB+0dp9KXMxj+nkkrwsN0MLzqibRq9YEWvwNE5vfU8yIqrQXJyj7D/+vjXtPB8Kr7tPhlgBW2AygVfyIX6WB41X1p1dkFtPh1RIufyhjXBi+d/avgl94whoDtLCqpacsvA2jxUxnlgdCnjv9u4HtLjWGv7SwqaWnLLwMstx1AqUR7Njxz6vn+6zeGdTC86pi2/DKAjoNSGWmRqag8zX97U0AHw6sD+YGNtkPEmha/diprqmtxhjwolXHWmKEjZ+lgeHWgtXh9YdshYk2LX8c+tB1AKVvaBsNv18HwypF25/tAi19ntPipjNZIfp9ZrfdMWhkeqoPhVVp2AtTi17EPbAdQyrYgWdmn+28/bkHoKB0Mn9kW2g4QD1r8Ova27QBKJQeRLweumf1I8FQdDJ+53rAdIB60+HXsfaDZdgilksU1wctm3xq8QAfDZ55NeH1p19MTtPh1qLKmOgjoyX6lovwx9OnjrwhcoYPhM0tatvpAi19X3rQdQKlk80x41tQv+K/boIPhM0badnjS4te5t2wHUCoZvW0qJ5zu/3VTwLg32M6i4k5bfhlIW35KdWK1GTby+Na78xtNrg6GT19NwBLbIeJFi18nKmuqtwL6zVapTtRROmB66/3Dd5i+79rOouJiEV5f0HaIeNHi1zU99KlUFxrJL5rZeq9HB8OnpbQ95Ala/A5FD30qdQhtg+FfD03UwfDpRYtfBptnO4BSqUHkwsDPZ/8jeIoOhk8PhjT/8q/FrwuVNdVLgK22cyiVKn4W/Prs24Jf1MHwqW8FXl9aX91Gi9+hPW87gFKp5A+hzxz/vcB339fB8CktrQ95gha/7njOdgClUs3T4eOOjQyG32E7i+qVl2wHiDctfof2PJB2F3JUKt4ig+EbdTB8ygkAz9oOEW9a/A6hsqZ6J3qVB6V6pW0wfJPJrbGdRXXbK3h9u22HiDctft2jhz6V6qU6SgdMa71/mA6GTxn/sR0gEbT4dY8WP6UOQyP5RbNa7z1qVXho2nekSHFh4EnbIRJBi1/3LALSutuvUvEWICvnNP/ts3QwfFJ7E6+v1naIRNDi1w2VNdVhYK7tHEqlPmcw/JzgyfN0MHxSetx2gETR4td9c2wHUCpd/DT4jZNvD35BB8Mnn4w43wda/HriBfTQp1Ixc3/os22D4RttZ1EAvI/Xt9Z2iETR4tdNlTXVAeAx2zniac6uXXxu7VqmrVzBtJUruGD9Ol7du3+SjtZwmJu31TJr5QqmrljOlZs3sSPY9RVPjDHcs2M7J61aydErlvO1jRtY5/fvu98fDvOTrVuYtnIFn1yzmjcaD/wc/PPOen6xLSNOQWSkp8PHHftF/7XrdTB8UsiYVh9o8euptD70OSg7i6sHDOBfI8v518hyZhQU8t3Nm1jZ2grArXV1vLJ3L3cOHcZfR4ykLhjkys2bu9zmn3fu5G+7dnHDoMHMGTGSfJeLb2zaSGvYmTfgUd9uPmxp4ZERIzmvpIQfb92CMc6poE1+P4/t3s1V/QfE94krqxaaCRM+7r9tb8C4N9rOkuEy5nwfaPHrqVeBLbZDxMspfYqY3acP5Tk5lOfkcNWAARS4XCxtbqYhFOLfvt38ZOBAZhYWMjEvj18OHsJ7Lc2839zc4faMMfx1104u79ePjxUVMT4vj1sHD6EuGOSlSItyjd/PqX36MDY3ly+VlLIzFGJXKATATdu28YMBA+njdifsNVB2rDTDy49vvTtPB8Nbswqv7wPbIRJJi18PRHp9/tN2jkQIGcPcPXtoNobJ+fl82NJCEJhVULhvnSNzcxmSlcWSTorfpkCAHaHQAY8pcruZlJe37zHjc3N5t7mZlnCYBY2NDHBnUep28/QeHzku4bSiorg+T5U8dDC8VRnV6gMtfr3xD9sB4mlFawtTVyxnyorl3LitlruHDmNMbi47QkGyRejbrhXWPyuLHaGOz/u1Le+flXXA8n5Rj/l8cQnjc3P59Lq1PLCznjuGDsUXDnPvjh38fOAgfrd9Ox9fs5qvb9zItoB2DEx3bYPhV4eH6GD4xPqX7QCJpsWvhyprqhcBq2zniJfynFz+Uz6KOSPL+UJJCdfUbmVV5JxfPGSLcN2gwbxw5GgeHVnO1IICfl1Xx5dLS6lubeGlvQ08Xj6KSfl53FK3LW45VPIIkJXzMf9vZr0RmqCD4RPjXby+RbZDJJoWv975u+0A8ZIjwsicHCbm5fH9AQMZn5vLw7t20d+dRcAY9kTOx7XZEQzS353V4bbalrfvEVrfxWMWNjWy2t/Kl0pKWdTUxEmFfShwufhEUV/ebmqKwTNUqUHkS4FrZ/8zOFsHw8ff720HsEGLX+/8CQgdcq00YICACTMxL48s4K2m/UMR1vpb2RoMMiU/v8PHDs/Opr/bfcBj9oZCLG1p6fAxzlCKbdwwaDBuEUIGgpHPvaAxel2pDPST4OUn/yZ4/uvG0PWYGtVbu4FHbIewQYtfL1TWVG8CnradI9bu2F7HO01NbA74WdHawh3b63i7qYlP9S2myO3mnOISbqurY2FTIx+2tPDzrbVMyctnclQhO2vtGl5saABARPhKaRl/rK/n5b0NrGht4ae1WxmYlcXH+vQ5aP+/r6/npMI+TMjLA+Do/HxeaGhgeUsLj+zexdGdFFmV3u4Lfe6EqwLfWaKD4eOiCq8vIw+pdHzsSXXH/cDnbIeIpZ2hED/duoXtoRBFLhfjcnN5cPgRHFfo9Nb86cCBuLbDlZs3EzCG4wsLuW7Q4AO2sdbvpyG8v1F8aVkZzSbMDbW1NITDHJOfzwPDjyDXdeD3rpWtrTzXsIf/lI/at+zjRUUsam7ioo0bGJWTw6+HDI3js1fJ7Mnw8cfW+ks/+kfOLwe6xPS3nSdNGJzPsYwkbQOKVc9UV1QKUAOMs51FpacXRhw7/45jvjjbdo5kMlY2rZub8zN3toSOsJ0lDbyI13e67RC26GHPXqqsqTbAH2znUCqTrDTDy09o/V1uk8ldbjtLGsjYVh9o8TtcfwEy8ni5UrZso2zg9Nb7htSbovdsZ0lhm4CnbIewSYvfYaisqd5Nms/3qVQy2ktB35mt901co4Phe+sBvL6M6LHeGS1+h+8+2wGUykQBsnJO9f9m1lvhSh0M3zMB4EHbIWzT4neYKmuq3wX026dSVoh80X/d7EeDJ82znSSFPI7Xl/HXCdPiFxu32A6gVCb7cfCbJ/8mcN4CHQzfLRnd0aWNFr8YqKypfhbQk+9KWXRv6OwTrg58WwfDd20JXp8eJkaLXyz90nYApTLdE+ETjr0g8PN1YSP1trMkqettB0gWWvxi5z/AR7ZDKJXp3gpPnPgJ/617Asa9yXaWJLMQry/tpmXsLS1+MRIZ9K7n/pRKAivMEaNObP1dtg6GP8C1tgMkEy1+sTWHNL7Wn1KppJayQToYfp95eH0v2g6RTLT4xVBlTXUI+JXtHEopx/7B8IPftJ3FMm31taPFL/YeBtbbDqGUckSuDD9jYbgiU3s5PofX97rtEMlGi1+MVdZUB4CbbOdQSu1ncLm+4L9+9mOhjBwMr62+Dmjxi4+HgPdth1BKHeiHgYwbDP84Xt9i2yGSkRa/OKisqQ4DP7CdQyl1sHtDZ5/w/cC33jMm7a/IEgausx0iWWnxi5PKmuqXgGdt51BKHezx8InTLgj8fG2aD4afg9f3oe0QyUqLX3z9CDLm8IpSKSXNB8MHgRtsh0hmWvziqLKmuhp4wHYOpVTH9g+Gz0m3wfAP4fXpmOMuaPGLPy+wx3YIpVTHnMHw96fTYPh64BrbIZKdFr84q6yp3o5Oe6ZUUttLQd9ZrfdOWBselA6D4X+E17fddohkp8UvMe4C1tgOoZTqnJ/s3FP9v53xdnh8Kg+GfwWv7y+2Q6SCLNsBMkFlTXVrdUXlN4HnbWdRmatl4zL2LPw3/m2rCe3dyYCzf07BuFn77t+94O80Vr9GqGE74soiZ/AYSk76CrlDx3e6zYb35tLw3lyCvm0AZPcfQclxF5A/+th96+x86UEal72EZOdRMvti+kw8Zd99jTULaFz2EgPPTY6+GQaX63z/DbN/m/37eee4XzvZdp4eagW+aTtEqtCWX4JU1lS/gDP1mVJWGH8L2QOPpOz0jj8fs8uGUXb6NxnytfsYdOGvySoexLZ/XkeoydfpNt1F/SidfTFDLr6LIRffRd7IydT95xf4tzsz/DWtWkhj9XwGnn8zpSd/lZ3P3bNve+HWRna/+lfKzvhW7J/sYfpB4Fsn3xE4N9UGw/8Sr2+F7RCpQotfYl0N6LF4ZUX+6GMpPekiCsYd1+H9hRNOJr98Ctklg8kZMJLSUy/D+Jvw163tdJsFY2aQP3oa2WXDyC4bRulJX8GVk0frFqfzZKB+I3lHeMgdMpbCCbORnIJ9rcRdr/yFoqPPJKvvwNg/2Ri4O/T5VBoM/xFwq+0QqUSLXwJV1lTXA1fZzqHUDC70qAAAIABJREFUoZhQgIYlzyG5heQMHNW9x4RDNH40n3CghdxhFQDkDBiFv3YVoZa9tNauwgRbySodSsumD/FvW03R1E/H82kctsfDJ067MHDN2rCRnbazdMEA38DrC9gOkkr0nF+CVdZUP1JdUXkhcKbtLEq117TqbXY89WtMoBV3n1IGfeFm3AXFXT7Gv30dtQ//EBP0Izn5DDz75+T0HwFA/pFTKZx4MrVVVyNZOfQ/62pc2bns/N/99Dvrauec4bvP4M7vS9nHv0vOgJGJeJo98kb4qImf9P9q7bM51zRlSXi47TwdeFCv2tBz2vKz41vAXtshlGovb8Qkhnz1bgZ/+XbyRk1l+5O3EWrc3eVjssuGOY/5yh0UHf1Jdjx7J/4dG/bdX3LChQy7/EGGXnofBeOOw/fmv8grn4K43Pje/CeDL/w1fSadQf2zd8T76fXacjNi1Amtd2c3m5xkO6dWC/zEdohUpMXPgsqa6g3oZUZUEnLl5JFdOpTcYRX0P/NKxOVi79KuOymLO9t5zOAxlM6+hJyBo2h456kO1w3Ub6Txo1coOfHLtGz4gLzhR+EuKKag4kT821YTbk3e02uRK8MP2mmKltjOEuVKvL6uv52oDmnxs+ce4C3bIZTqkjGYUM9OJZlOHmOMof5/91F66mW4cvLBhDHhSGfKtp8mfLiJ46qBwuKZrfdWrkuOwfBz8foetR0iVWnxsyRy2aOvAI22s6jMEPY349+2Bv82Z76FoG8b/m1rCO6pI+xvYdf8Klo31xD01dFau4odc+8i2FBPwfgT9m1j25xr2LP46X2/75r/EC0blznb2r6OXfMfonXDBxROOPmg/e99/3+48/tSMGYGALnDKmlZv5TWzTXsWfQk2f1G4MrrE98XIQb8ZOeeYn8wfAPwbYv7T3na4cWiyprqldUVlVcCf7KdRaU/f+1Ktv1j/5SPu1523naFR32Mfh//DoGdm9j+xEuEmvfgzu9LzuCxDL7wtgM6oQR21ZLbvH+q2lCjjx3P3EGocSeu3EJyBpQz8PybyB919AH7DjXuwvfmowz+8u37luUOHU/f6WdT99iNuAqK6X/W1fF66jHXNhj+juz753/evWC2hQiX4fWtt7DftCHGGNsZMl51ReVjwDm2c6jk8sKIY+ffccwXbXywqh640v3vBVdl/XumSMIaE/fh9X03QftKW3rYMzl8A9hsO4RSqud+FzrnhB8GvpmowfDvAN9PwH7Snha/JFBZU70T+BIQsp1FKdVz/w6fNO3LgWvWxHkw/G7gfLw+fxz3kTG0+CWJyprqV4EbbedQSvXO6+GjjjrT/6vdQeOK15Xhv4rX1/lcc6pHtPgll18CL9kOoZTqnRoz4sgTW3+X1WxyVsZ403fi9T0R421mNC1+SSQy/OHLOLM2KKVS0Fb6DZ7eet/AXaZPrAbDv4XO4hJzPSp+IjJPRO7qYPklIrI78m+viBgR+UO7daZElpdHfi+P/D4lap0iEXlFRD4SkeFR69SJSFG77S0REW+7ZRNF5FER2S4irSKyQkRuEpGCqHXmiMhz7R73ich+2m/PKyIb2uXtVpbeqqyprgXOxrk2l1IqBTVQWDyj9b7K9eGBhzuRRT3OeT6dtDrG4tXyawEuFZGx3X2AiAwAXgEKgRONMdHHzYuAHx7i8TOBhUAOcBYwDvg5cAnwgojkRFZ9BTheRKK7JZ8CbARObrfZUyLrRztklsNVWVP9FnpRSqVSmp/s3JP9d0x/Jzzu1V5uwgBfwevbGMtcyhGv4rccp2j8sjsri8gRwGuADzjVGFPfbpV7gO+LSIcX/hIRAf4MVAOfN8a8bYxZb4z5F/BpYBbOtfSI5OoDHBu1iZNxroU1Q0TyItv8f/buO06uqnD/+OfsprdJTxYQEEEyFOkI0hHEL6NYaSI/EBFU/AqoSFHhAl9lkCJVRFBAqiCC0kRagqCAiXRmwZRJSEJ6clO3zvn9cWbJZrI1OzNn5t7n/XrNa7J3pzwzu5lnz7ltEPBJNi6/LrMUS7I+czvwq1I+h4iUlqWm5qtNwYEPte63KUeDuZwgfLzooQQo7Tq/84CvGGP27OZ22wMv4k7GeKS1tqOzHdwLTAcu7OQxdgV2AK62dsODA1prXweeBo7Pf/0eMB83qiM/hbk78ACQxRUlwKeAgWxcft1lKaZzgCfL8DwiUkJnN59x0DUtX/6HtT3enel5dPD7kipZ+Vlr/wPcD1zezU3/gCuTo621na3nsrgyPc0Y87EOvv/x/HWmk/tn2t0GXKEdnP/3AcB71trFuF+4tuUHA7OstYWHEOouS9Ek6zOtwHFApZ1GRUR66ZqWrx5wTsvp06xlXTc3nQ58hSDUfr8lVOqtPX8KHGCM+UwXt/krroC+3NUDWWufBF4ALu3iZqaHuSbj1vv1x5Xc5PzyKWxYfoWjvt5kKYpkfWYFcBRuB1cRqWJ/aj1o7xObz5/exc7wS4D/IQiXlDNXHPW2/FYCHZ3WeSRufd0GrLUzgFtw69M6K6afA5cA9xhjjunm+c8DjjXG7FawvG1klOzkfkk2HD21bVizF276s20+fgpuvd9o3Pq+ZzchS9El6zPv4qZt9ZegSJV7Ibfzzvmd4QsPabgO+DxBON1Hrrjpbfm9i1s/Vmh3Op+auwQ35XhcZw9qrb0UCIC7jTHHdnG7V4A/48q0vdeAeuBsY8wGr8kYswtwGG5dXdvjzMBt3XkUbn3hlPzyebhjbP4Qt9VohyO/brKURLI+8zfgf8vxXCJSWvmd4Wvb7Qzv9vENQp3js0x6W343AR83xlxnjPmEMWZ7Y8wPcKOSqzq6g7V2IXA18P2uHtha+3PgZ7gCPL6Lm/4EOBS3oUzbfS3wTdxGLw8aY/Y2xmxpjDkaeAT4F1C4f+JzuPNhTc9nbDMFVzLvWWvnd5W5oyyllKzP3ER5NrQRkRL7gDETP9l4w/jldtjrwA8Jwj/7zhQnvSo/a+1M4EBgEm4LypeBY3Abq/yti7teCXS0FWfh46eBC4A7jTFf6+Q27wG/BwYVLP8nsA9uavAJ3Erjy4A7gMM72JjmOdw+e5MLlk/JL+901NddllJK1mcuBa4t1/OJSOmsZFjik403PkkQbnTwECktnc+vCmUmJQ2u1E/0nUVKR+fzi4U7gG9k0yl9EJeZju1ZhZL1GQucgpvSFZHq9AhwqorPD5VflUrWZ1pwU86beugkEfHnOeDYbDrV4jtIXKn8qliyPtOAO3zbq76ziEiPPQd8LptOdbezu5SQyq/KJeszK4EjgDd9ZxGRbrUV31rfQeJO5RcByfrMYtzO+tN8ZxGRTqn4KojKLyKS9ZmlwKdx+zSKSGVR8VUYlV+EJOszIfAZerCPooiUjYqvAqn8IiZZn1mNO5lvVwcdEJHy+Dsqvoqk8ougZH1mHfAF4GHfWURi7G5UfBVL5RdRyfpME3A0cJ/vLCIxdBVwYjadavYdRDrWz3cAKZ1kfaYlMyl5AvABcLbvPCIxYIEfZdOpq30Hka5p5BdxyfpMLlmf+QHwHXQ+QJFSaga+ruKrDiq/mEjWZ36D2xBmpe8sIhG0Ckhl06l7fAeRnlH5xUiyPvMksB8w23cWkQiZDRyYTaee8h1Eek7lFzPJ+sxbwCeBV3xnEYmAKcBe2XTqNd9BpHdUfjGUrM8sBA4GHvQcRaSa3Qgclk2nFvsOIr2n8oup/L6ARwMXAznPcUSqSRPwrWw69T2dkqh6qfxiLFmfscn6TAB8FtBfryLdWwAckk2nbvUdRPpG5Sck6zNPAbsC//CdRaSCvQTsmU2n/uk7iPSdyk8ASNZn5uNOi3Q5bkddEXFywGXAAdl0ap7vMFIcOsKLfChZn2kFzstMSv4D+AMw2nMkEd/m4w5T9qzvIFJcGvnJRpL1mceA3XDTPCJx9Siwi4ovmlR+0qFkfWYOcCBwCaAt2iROGoEzs+nU57Pp1BLfYaQ0VH7SqWR9pjlZn7kIt1P8m77ziJTBO8Ans+nUdb6DSGmp/KRbyfrMf4A9gf9Do0CJpmbc7/du2XTqdd9hpPS0wYv0SP78gD/LTEo+DNwO7OQ3kUjRTAW+mU2n3vAdRMpHIz/plWR9ZhqwB/BzNAqU6rYO+DGwj4ovfjTyk17LjwJ/mh8F3gzs7jmSSG9NwR2i7L++g4gfGvnJJkvWZ6YCewGnA9oqTqrBEuA03CHKVHwxppGf9EmyPpMDfpuZlHwAt1vEd4Bav6lENtKMOwvDxdl0aoXvMOKfyk+KIlmfWQ78b2ZS8rfAtbhDpYlUgr8BZ2fTqXrfQaRyqPykqJL1mTeBQzOTkkcDVwJbeo4k8fUe8INsOvWY7yBSebTOT0oiWZ95AEgCFwErPceReFkG/BDYScUnndHIT0omWZ9ZC1ySmZS8ETgPOAMY7DeVRFgIXA1ck02n9AeXdEnlJyWXrM8sBc7JTEr+CvgJcCowwG8qiZBVuPXMV2ljFukplZ+UTf6cgWdkJiXTuJHgN4GBflNJFVsD3ABckU2nlvoOI9VF5Sdll6zPvI8rwctYX4KD/KaSKrIad3CFX2bTqUW+w0h1UvmJN8n6zFzge5lJyYtxO8p/F6jzm0oq2HzgOuBmTW9KX6n8xLtkfWYx8H+ZScnLgWOBs3DHDxUBeB24Brgnm041+Q4j0aDyk4qRrM80A3cBd2UmJffHleAX0RFj4igH/BW35eYU32EkelR+UpGS9ZkXgBcyk5JbAd8DTgbGeg0l5TAHd8qs27LpVNZvFIkylZ9UtGR9ZjZuN4kLgBTw//LX2lUiOhqBh4DfA89k06mc5zwSAyo/qQr5KdGHgYczk5JjgONwRbi312DSF6/iCu/ubDq13HcYiReVn1Sd/E7zNwI3ZiYlJ+FK8OvAR7wGk56oBx4EHsimU6/7DiPxpfKTqpasz9QDF2QmJX8K7Asclb9M8hpM2nsdV3gPZtOpd3yHEQGVn0RE/ryCL+Yv52YmJbdlfRHuj7YYLScL/Jv1hTfDcx6Rjaj8JJKS9ZnpuIMcX52ZlBwN/A+uCI8AEj6zRdRs4Kn85dlsOrXEcx6RLqn8JPKS9ZllwN3A3ZlJyRpgF+AA4MD89XiP8arVCuA58oWXTaeme84j0isqP4mV/PToq/nLdQCZScntcSXYVohb+8pXoXJABnglf3kZeCObTrV6TVXAGHM7cBJwvrU23W75F4GHrLXGGHMwrrQ7UmetXZC/zwjgHODLwDbAWmAm8ABwi7VWW6dWOZWfxF6yPvMu8C5wK0BmUnILYHdgZ+AT+ct2xGe94VzcOru2opuaTadW+Y3UYw3AucaYm7spqO3Z+CTLiwCMMaOBF4ARwM+AabhzBW4PfAP4Gm5rY6liKj+RAvkDbs/FHV4LgMyk5CBgB9aX4c75r+sA4yFmX7UA03G7HmTaX1dR0XXkaWBb4Hzgx13cbpG1trODY/8C2BL4uLV2frvls4G/G2Oq8ectBVR+Ij2QrM80AP/JXz6UmZQcAGwBbIX7wGy7bvv3R/Bz9vqlwLxOLrOA6dl0qtlDrlJrBS4A7jHGXGetndubOxtjanAHV7+roPg+ZK21fY8pvqn8RPogWZ9pwq0LmtnZbTKTksNwW5iOzF93dumHm1qtAWpX9R/yAZDFrXNrzV+vxU3XrergehVuQ5QPsulUQ3FfafWw1j5kjHkNuBh3rsiOzC0YwM221u4IjMP9nN5t/01jzDTctCfAI9ba44ubWspN5SdSYsn6zGrcCVjn9eZ+P8lfZJOcCzxrjLmyk+8fgPtjoU13o+Av4Y4nezl+RvJSZCo/EYkca+3zxpgngctwZ4koNKuTdX6LcaPn7dsvtNbOATDGrMKNDKXK1fgOICJSIucBn8cd9q5HrLU54H7g68aYzUoVTPzTyE9EIsla+6Yx5m7g+x18e7wxZlDBsqXW2mbcBjMHA68YYy4EpgJrcFv57gu8VbrUUi4qPxGJsgtxW28WereDZfsCL1lrlxpj9satNzwH+ChuY6P/An8ErilRVikjo612RUQkbrTOT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn1QFY4w1xgS+c7QxxpxjjJlpjGk1xrzmO08pGGNOzr/vW/vOsimMMVljzO2+c0hlUvlJ+w+5tkuLMWaeMeZ2Y8zmvvN1xBjzKWNMYIwZ6eG5PwP8EngR+AZwQRe3/Zox5qwyZNoh/35sXernKhZjzJGV9AeNxEs/3wGkolwIzAIGAfsAJwP7G2N2stY2+AzWgU8BFwG3AyvK/NyHAjngm9bapm5u+zVgJ+CaEmfaAfd+TAayJX6uYjkSOAMISvT42+N+TiIbUflJe09Ya6fm/32rMWYJcC5wFHC/v1gVZzywrgfFJ2VmjDHAIGvtOmtto+88Urk07Sld+Uf++mPtFxpjJhlj/mSMWWaMaTDGTDXGHFVwm/7GmIuMMf/N32apMeYFY8zh7W4z2RgzufBJ89Ot2c5C5afKrsh/OavddO3W+e8fnn+uFcaY1caYd40xv+juxRpj+hljfmaMmWGMacyvM/qFMWZgu9tY3FTn0HbPe3InjzcZSAFbtbtttt33BxpjLjbGTM8/3/vGmF8WPN8d+fcvWfDYTxpjlhtjNss//wP5bz3X7rkO7ub1TjLG3G+MWWyMWZd/n37ezX06XPdauH6tu59//rZntHtMm39v2+5fY4w5yxjzdv7+C40xNxtjRnXwvI8aY44wxkwF1gGnd5KpbXp/P2PM1fnXvcYY85AxZlzB49bkp5HnG2PWGmOey08taz1iRGjkJ13ZOn+9vG2BMWZH3LqueUAaWAMcAzxsjPmKtfah/E0D4HzgVuAVYASwJ7A78FQfc/0Z+DhwPHA2sCS/fHE+36PAG7hp3EZgW2C/HjzurcBJwJ+Aq4BP5l9DEvhS/jYnAqcBewOn5pf9s5PH+zmQALbI5wRYDe7DFfgrsD/wWyAD7Jy/3ceBL+ZvfyZumvUOY8y+1tpWY8zpwGeAE621840xzwPXAd8HfpF/LNpdb8QY8wncHzfN+efP4v7I+Tzwk87u1wsBXf/8bwY2Aw7HvaeFbsZNu9+Ge20fBb4H7GaM2c9a29zuttsD9+bvcwvwbjfZrsf9Tl+M+x0/C7gBOLbdbS4Dfgw8AjwJ7JK/HtTNY0u1sNbqEvML7kPGAp8GxuI+rL8CLAIagC3a3fZpXLEMbLfM4ArxvXbLXgMe7eZ5JwOTO1h+O5AtWGaBoN3XP8ov27rgdmfll4/t5XuwS/5+txQsvyK//JCCfKt7+LiPFr6W/PKvA63A/gXLT88/36faLftMftlPcCWwCnio4H5fzd/m4B7mmgKsBLYsWG46+L3Yut2yDX4O7ZZngdt7+fO/wX0EbbR8//zzfK1g+RGFy/PPa4EjepCp7fU8VfA6rwZagET+6wm4PwoK3+OL8ve/vavXpUt1XDTtKe09DSwG3seNftYAR1lr5wIYY0bjRiH3A8ONMWONMWOBMbi/ircz67cOXQHsaIzZrsyvoW3jly/kR1c9dWT++uqC5Vflr1N9SrWxo3Ejs/q29zH/Xj6b//4hbTe01v4dN6q5EDfqbSA/tbcp8lN8BwK/t9bOaf89m/+UL4K+/PyPBkLgqYL3Zhpu5HxIwe1nWWuf7MXj/7bgdf4DqAW2yn/9adys2K8L7nd9L55DKpzKT9o7AzcN9VXgcdwosP1GA9viRnmX4kqy/eXi/G3G568vBEYC7xlj3jTGXJGfaiu1P+JGobcCC40x9xljjulBEW6F2zJwevuF1toFuA/yrTq6Ux9sB+zIxu/je/nvjy+4/Y+AZcCuwPettYv68Nzb5K/f6sNjdKcvP//tcNPFi9j4/RnGxu/NrF5mm1Pwddu0ftv6xLafdeHvwrJ2t5Uqp3V+0t4rNr+1pzHmYeAF4B5jzPbW2tWs/2PpStxIryPTAay1zxtjPgZ8ATdtdypwtjHm29baW/O3tbgyLVS7qS/AWrvOGHMgbnSQAj6LW5fzrDHmM9ba1u4eYlOfu5dqgDeBH3Ty/fcLvt6N9R/6O+PWcVWSDX5mPfz5d6YGV3wndPL9xQVfr+tl1s5+Bzr6XZSIUvlJh6zbsOJ84DnchgZpYGb+283W2qd78BjLcBss3GaMGQY8j9sQou3DbznrRyHt9WSU1WlJWWtzwDP5yw+MMRfgNj45BDe125HZuA/d7Wi3oYgxZgJuBDO7B5l6k3MGbj3jM91NNRpjhuLex3dwG9f82BjzkLX23z14no60/Rx36sV92izHvR/t8w0A6gpv2IOff1fvzWHAi9ba3hZbMbT9rLel3ajSGDOG9aNDqXKa9pROWWsn47bUO8sYMyg/1TYZON0Ys9GHXfvNxfMfFO0fazVuVDiw3eIZwKSC++1Cz7bMXJO/LvwgHt3BbdsOPzawg++1eTx/XXg0lraR2WM9yNSRNbgpvEL3A5sD3yr8hjFmcL7w2lwObInbEvUHuA057mi/SwSdvB8dsdYuxhXRKcaYLQueu7vRzwzc+sL2TqNg5NfDn/+a/G0LM9+ff7yfFT65cbujlPqoPs/gNoD5TsHy75X4eaWMNPKT7lyB24fsZOA3uPWCLwBvGmNuwY0iJgD74rYS3SV/v3eM289tGm5d1Z64dYk3tHvs3+M+zJ80xvwON633beBt3KbxXZmWv/65MeY+3NZ5jwAX5qc9H8P9BT8e+C4wN5+7Q9ba140xdwCn5T9cp+B2ZzgJeNha+1w3ebrKeawx5mrg37itRB8B7sTtIvIbY8whuPWUtcCk/PIjgKnGmEPz+S+21v4HwBjzDdwfIZfiNscHV/CtwLnGmARuXe2zXawb/D7u/fiPMea3uBHO1rip4l27eD235jM/iNtqcpd81iUFt+vJz7/tZ3idMeZJoNVae5+1doox5mbgfGPMrsDfcT/f7XAbw5yJ2yCrJKy1C40x1wI/NMb8Ffgb7nX+D+51lmtqXErJ9+amuvi/sH4T8D07+F4N7i/26UBtftk2wB3AB0ATrlgeAb7S7n4/AV7GTZOtxU0lXgD0L3j8E3CjiUbgVdz6odvpZleH/LKf5p+7Nf/9rXFboz6M2w+xMX99D7BdD96HfrgNNWbmX9cc3H5zAwtudzs939VhKHB3/n2w7V8X0B9XXm/htuBcBkzNZxgBDMeN8qYB/Qoe9+r8696n3bJT8+9lCz3Y7QG3wc2f89nWAfXAJR38Xmxd8PuQxq13W4Mrho+x8W4F3f78cWV/HW79Xo6C3R5wo+Kp+fuvxO1iczlQ1+42WTrZpaKDTG2vZ8+C2x1c+H7ls12C+x1fixsNTsKV302+/8/q0veLyf+gRUSkC/kZgeXAT621XR4JRyqf1vmJiBQwxgzuYHHb+uDJZYwiJaJ1fiIiGzvWuGOmPo7bsX5/3OH0/m6tfdFnMCkOlZ+IyMbewK07/TFu/etC4FrcemaJAK3zExGR2NE6PxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5SciIrHTz3cAkcgLEiOBccCwdpehXfx7KGCARqApf1347/ZfNwBLgPnAB8BSgtCW58WJVCdjrf6PiGyyIFELbA5sBWzZyWVEmVM1AwtwRdh2md/u33OBdwnChjLnEqkYKj+RnggSA4Gdgd3yl52ArYHNgFp/wTZZKzATeKvg8h5B2OIzmEg5qPxECgWJEcCuuJLbPX+dJB6rCZqAd9mwEKcRhPO8phIpMpWfxJubttwTOAjYC1d02+DWucl6M4ApwGRgCkE4x28ckb5R+Um8BIka3KjukPzlAMq/Ti4KsrgydJcgnOk3jkjvqPwk+oLEBOCz+cvhwBi/gSLpfdaPDB8nCD/wG0ekayo/iaYgsRfwJeB/gF3QNGY5WeAV4GHgYYKw3nMekY2o/CQ6gsQOwPHAccC2ntPIeu8CDwH3E4Sv+g4jAio/qXZBYmtFiHwiAAAgAElEQVRc2R0PfMJvGOmB/wL3A38kCN/0HUbiS+Un1SdITASOwRXePp7TyKbLAHcAvycIF/sOI/Gi8pPqECQGAEcD3wAOpjp3LJeONQF/Bn5DEE7xHUbiQeUnlc1tqfnt/GWi5zRSehngZuAOgnCF7zASXSo/qUxBYk/gTNz05gDPaaT81gH34UaDr/gOI9Gj8pPKEST6A18Fvo/W5cl6/8GNBu8kCNf5DiPRoPIT/4LEOOB04Du4A0WLdGQhcBXwa4Jwje8wUt1UfuJPkKgDfgp8ExjoOY1Uj8XA1cANBOFq32GkOqn8pPyCxFjgPOC7wGDPaaR6LQWuAa4jCFf6DiPVReUn5RMkEsCPgLNwZy0XKYblwLXAtdpCVHpK5SelFySG4rbc/BEwynMaia4QuB74FUG4zHcYqWwqPymdIDEItxHLecB4z2kkPkLgEuB6grDZdxipTCo/KT53zrxTgIuALTynkfjKAGcShE/5DiKVR+UnxRUkdgN+A+ztO4pI3sPADwjCWb6DSOVQ+UlxBIkRwKXAGei4m1J5GoArgcsIwrW+w4h/Kj/puyBxHG6/qzrfUUS68T5wDkH4R99BxC+Vn2y6ILEdcCNwuO8oIr00Gfi+zikYXyo/6T23Fef5wLnoyCxSvVqBNHCxtgqNH5Wf9E6QOBz4NbCt7ygiRfIqcCJB+LbvIFI+Kj/pGTfauwL4nu8oIiXQiDvO7NUEYc53GCk9lZ90L0jsBNwL7OQ7ikiJPQ+cRBBmfQeR0qrxHUAqXJD4X+DfqPgkHg4E3iBInOo7iJSWRn7SMXeOvduAlO8oIp48CpxKEC70HUSKTyM/2ViQ+CzwJio+ibfPAW8RJL7sO4gUn0Z+sl6QGAhcDnwfMJ7TiFSSK4DzCcJW30GkOFR+4gSJ7YE/Arv4jiJSoZ4CjtPpkqJB5ScQJI7AFV/CdxSRCjcL+BJB+LrvINI3WucXd0HiTOAxVHwiPfFR4J8EieN9B5G+0cgvroJEf9xxOb/lO4pIlboKOFfrAauTRn5lYIw50BjziDFmvjHGGmO+6DVQkBiDW3+h4hPZdD8Ensz/f5Iqo/Irj6HA67hz3fkVJHYAXgEO8h1FJAI+DUzNn8RZqoimPcvMGGOBL1lrHy77kweJI3GHKRtR9ucWiba1wNEE4eO+g0jPaOQXF0Hih8AjqPhESmEI8BeCxAm+g0jPqPyiLkgYgsSNwJXo5y1SSv2AOwkSOvNJFdCHYZQFiVrc8Tm/6zuKSEwY4HqCROA7iHRN5RdVQaIfcA9wku8oIjF0EUHieoKEDhNYoVR+UeSO0fkgcIzvKCIx9j3grvw+tVJhVH5lYIwZZozZ1Riza37RR/Nfb1n0JwsSg4G/AkcV/bFFpLe+Bjyc/38pFUS7OpSBMeZg4LkOvnWHtfbkoj1RkBiGOweZ9uETqSwvAp8jCFf4DiKOyi8qgsRI4AlgH99RRKRD/wEOJQhD30FE057RECTGAs+i4hOpZLsDj2oKtDKo/KqdK77nAB1eSaTy7Q/8WRvB+Kfyq2ZuHd8TwE6+o4hIj30WuJsgoc9fj/TmVyu3O8NfgD19RxGRXjsauNl3iDhT+VUjd+SWe4FDfUcRkU12KkHiSt8h4krlV51+A3zJdwgR6bMfEiR+6jtEHKn8qk2QuBg41XcMESmaS3Uw7PLTfn7VJEh8E7jVdwwRKToLnEQQ3uk7SFyo/KpFkPgs7nx8/XxHEZGSaAYOJwin+A4SByq/ahAkdgOeB4b5jiIiJbUU2IsgnOU7SNSp/CpdkKgDpgF1vqOISFm8DexLEK7yHSTKtMFLJXPn5LsfFZ9InOwI3Kud4EtLb25luwJ3OCQRiZcUcJnvEFGmac9KFSSOBe7zHUNEvDqGIHzAd4goUvlVoiCRBF5BG7iIxN1qYB+C8G3fQaJG5VdpgsRwXPFN8h1FRCrCf3FbgOo8gEWkdX6V5/eo+ERkve2AOwkSxneQKFH5VZIg8QPgq75jiEjF+TzwQ98hokTTnpUiSByAOxu7juAiIh1pwk1/vuE7SBSo/CpBkJgAvAZM9B1FRCram7gCbPQdpNpp2rMy/BYVn4h0b2fg575DRIFGfr4Fia8DOpK7iPRUDvg0QTjZd5BqpvLzKUhMBN4BRvmOIiJVZQ6wM0G40neQaqVpT79uRsUnIr23JXCD7xDVTOXni5vuPMp3DBGpWicSJI72HaJaadrTB3eaorfRqE9E+mYZbvpzvu8g1UYjPz803SkixTAad1Qo6SWVX7kFiRNxR2sQESmGIwgSJ/gOUW007VlOmu4UkdL4ANheZ3/vOY38yusmVHwiUnx1wIW+Q1QTjfzKJUgcAfzNdwwRiaxm4BMEYb3vINVAI79yCBK1wFW+Y4hIpPUHrvcdolqo/MrjdGBH3yFEJPIOI0jotGg9oGnPUgsSI3FnYh7rO4qIxMIcIEkQrvUdpJJp5Fd6P0PFJyLlsyVwge8QlU4jv1IKEtvidm0Y4DuKiMRKI7AjQTjDd5BKpZFfaV2Jik9Eym8gcK3vEJVMI79SCRKHAM/6jiEisXYoQfic7xCVSCO/UggSNcCvfMcQkdgLfAeoVCq/0jgF2MV3CBGJvQMJEof6DlGJVH7FFiT647bwFBGpBBf5DlCJVH7F93XcpsYiIpVAo78OqPyKya3rO893DBGRAoHvAJVG5VdcXwU+7juEiEiBAwgSn/YdopL08x0gYs73HaAUtr5mFbPDjXeJ+e6e/bkxNZjTH1nH07NamL/KMmyA4VMfqeXywwYyaWxtp48ZTG7gvrdaeH9ljgG1sEddLT8/dCCf3ML9Sja2WE59pIG/1DczcVgNv04N4rBt1v+6XvFiI3PCHNcfObj4L1gkmi4CnvEdolJoP79iCRJHAo/5jlEKi9fkaG33a/LWohyH37mW504awsFb9+O305qYNLaGLRM1LFtnCSY38tqCVmadOYzaGtPhY97zZjPjhxq2GVXDumbLr15q4oF3mpn+v8MYN7SG619u4qapTTxw9GCemN7CL19sYuGPhmGMYdbyHEfctZappw1lxMCOH19EOnQYQagCRNOexRTZY+mNG1rDxGHrL4++18LHRhkO2sqN7E7bYwAHbtWPrUfWsHtdLf936EDeX2nJruj8D6uv7dyfw7bpxzajathxfC1XHzGIlY3wxsIcAJklrRy1fT92HF/LGXsNYPFay5K17vG+89g6Lj9soIpPpPcC3wEqhcqvGILEgcB+vmOUQ1Or5a43mjlltwEYs3H5rGmy3PZqMx8dafhIomfl1NRq+e20JhIDYZeJ7ldylwm1vDCnlXXNlidntFA3zDB2iOHuN5oZ1M/wpWT/or4ukZjYX1t+OlrnVxyRHfUVeri+hRUNlpN33bB8fv3vJn78VANrmmH7MTU8deJQBtR2XX6PvtfMcX9ax9pmqBtueOrEoYwd4srvlN3688bCVnb49WrGDjHcf/RgljfAhZMbmHzSUH76bAP3vdXMx0bX8PujBrP5CP0dJ9JDZ6JDL2qdX58FiT2Aqb5jlMsRd61hQK3hkeOHbLA8bLAsWpPjg9WWK//ZxLxVOV48ZSiD+nVegGuaLB+stixZm+OWac08m23h5VOHMn5ox0X2jb+sY9cJNXx0VA0XPNPIy6cO5ZcvNvLW4hwPHjOkw/uIyEZagW0Iwjm+g/ikP5f77lzfAcpl9oocT89s5dTdNp5yTAwybDemlgO36sefjhlM/ZIcD2Vauny8oQMM246uYZ8t+vG7LwymX43hd/9p7vC2z81q4e1FrXxv7wFMzrZy5Hb9GDrAcMyO/ZmcbS3K6xOJiVrgdN8hfFP59UWQmAB80XeMcrnttSbGDzWkPt71bLm17tLY2rtZhZy1Hd6nocVyxuMN3Py5wdTWGFpz0Jzvu+YctOY0eyHSS98kSMT6dGsqv745CYjFlhc5a7nttWZO2qU//drtvjBzeY7L/tHItPmtzAlz/PP9Fo5+YB2D+xuO3G59SU66YTUPZdyobk2T5YJnGnhpbguzV+SYNr+VU/6yjnkrLUfvsPHbeemURo7crh+71bmtS/fbspY/1zfzxsJWbnilif221KprkV6aAHzFdwif9KnRN9/0HaBcnp7ZypzQckrBlOegfvCPOa1c83ITy9dZJgwzHLhVLf88ZcgG6+7eXZojbHQjtNoaqF+S447X17FkrWXMYMNem9fyj28MZcfxG+4Y/9aiVu5/p4XXTh/64bKv7tCPydl+HHDbGrYfU8M9X9H6PpFN8F3gXt8hfNEGL5vK7d4wxXcMEZE++ARB+KbvED5o2nPTfct3ABGRPvqu7wC+aOS3KYLESGA+oANLikg1Ww1sRhCu8h2k3DTy2zQnoOITkeo3DPh/vkP4oPLbNJryFJGo+LbvAD6o/HorSOwJ7OI7hohIkexEkNjRd4hyU/n13qm+A4iIFNnRvgOUm8qvN4LEIOB43zFERIpM5SddOhwY4TuEiEiR7UCQ2MF3iHJS+fVObI7jKSKxE6vRn8qvp4JEDfA53zFEREpE5Scd+hQw3ncIEZES2ZEgkfQdolxUfj33Bd8BRERKLDajP5Vfz2l9n4hE3TG+A5SLyq8n3A6g2/qOISJSYrGZ+lT59YymPEUkLmIx9any6xlNeYpIXHzGd4By0CmNuhMkNgPmAsZ3FBGRMmgGRhGEa3wHKSWN/Lp3FCo+EYmP/sB+vkOUmsqve4f5DiAiUmaH+g5Qaiq/7kX+LyARkQKH+A5Qalrn15UgsS3wX98xRETKrBUYTRCu9B2kVDTy69r+vgOIiHhQCxzgO0Qpqfy6pilPEYmrSE99qvy6ppGfiMRVpMtP6/w6EyTGAot9xxAR8SQHjCUIl/sOUgoa+XVOU54iEmc1wIG+Q5SKyq9zmvIUkbiL7Oegyq9zGvmJSNzt4jtAqaj8OhIkBgF7+I4hIuLZJ3wHKBWVX8f2AAb4DiEi4tkEgsQ43yFKQeXXscj+tSMi0kuR/DxU+XVsR98BREQqhMovRnbyHUBEpEKo/GJEIz8RESeS5acjvBQKEhOABb5jiIhUiAZgGEHY6jtIMWnkt7EdfAcQEakgg4DtfIcoNpXfxj7uO4CISIWJ3NSnym9jKj8RkQ2p/GIgcsN7EZE+itznospvYxr5iYhsaEvfAYpN5ddekKgFtvEdQ0Skwqj8Im4zoL/vECIiFWYiQSJSn40qvw1N8B1ARKQC1QBb+A5RTCq/DY33HUBEpNJYy+oFdtTmvnMUUz/fASpMJE/dISLSEWvJ5TCL1zFw6QqGrVpgRzfOseNzM3N1/WbazYbMshMT79tx41YzZASwZdZ34CJS+W1IIz8RiQRrWd1Ev8WrGbxiiU2snWvHNWftRDPD1g2caeuGz8lNGL2A0eNz1EygZ6t8JpY6czmp/Dak8hORimYtrfnR2rLldtiqBYxunGMn5Gbm6vrNsHVDsrZu5Pt23Lg1DB4ODCviU9cV8bG8U/ltSNOeIuKNtaxqot/iVQzJj9bGtmRtXc0MWzdwZq5u+Pt2/OgFjB6Xo2Yi5R+JaeQXYRr5iUjRudFazaK1G47W7MxcXf+Ztm5INr9uLT9aG+47byci9fmo8ttQpH64IlJ61rKyif6LVzE4XGxHrp1rx7bMshNrZtm6QTNzdcPm2AmjFzJqXI6aOqp76rBSS3mTqPw2pPITEQCspaWVmvy6teGrPmB045zceGbZtnVrE0fOdaO1EcAI33nLQOUXYVrnJxID1hI20n/JKoasWGwTDfPs2OZZdmLNTLvZwJm5uuFz7IQxCxk5zlb/aK2YIlXwKr82QWI47qSNIlKl8qO1ResYuLxttDY7N97OtJu1rVsbOdeOG7+WQQkg4TtvldHIL6Ii9YMViZqC0dq6uXZcyyw7sWaG3WzQrFzd8Dl2/JhFjBxrqdkMd5xeKa5IfUYaa63vDJUhSGwJzPYdQyRu2kZrbkvI4as/YEz70drQrJ04cp4dO24tg4b6zioMyaZT63yHKAaN/Nar9R1AJGryo7XFKxkSLrYjOxqtjV1MYoxGa1VjBKDyixiVn0gPWUuzG60NWrbMDl/9gR3dNNtOsDPtZv1n2Lqhs+2EUfPsuHHrGKh1a9EyHFjoO0QxqPzW03shAuQsK5rov3glQ8NFbrTW6raErBs8K1c3bI4dP3YRI8eC2RyI1JH+pVuR+ZyMzAspAo38JNLyo7WFaxi03I3WxjTNthOYZev65/dbGzXPjh3fwMCRwEjfeUVKSeW3nspPqlbOsryRAUtWMqRttJZzR/DfbNDMXN2IOXb8mCUkxoLZgoidlFRkU6j81tN7IRXHWppaqF20loHLltkRa/Lr1mhbt5a1E0fOt2PGNzBwFDDKd16RaqEP/PU08pOK8ljrJyef0fz9gzRaEym+Gt8BKojKTyrKWjvQgDG+c4hEkcpvPZWfiEhMqPzWU/mJiMSEym+9Rt8BREQqXKvvAMWi8lsv9B1ARKTCrfYdoFhUfuup/EREurbSd4BiUfmtp/ITEelcLptOrfEdolhUfm2CcC3Q7DuGiEiFisyUJ6j8Cmn0JyLSschMeYLKr5DKT0SkY6t8Bygmld+GVH4iIh1T+UWYyk9EpGOa9owwlZ+ISMeW+A5QTCq/Dan8REQ6Ns93gGJS+W1oue8AIiIVSuUXYXN9BxARqVAqvwib6TuAiEiFUvlF2CzfAUREKpTKL8I08hMR2ZgF5vsOUUwqv/aCcCWw1HcMEZEKsySbTjX5DlFMKr+NaepTRGRDkdsYUOW3MU19iohs6D3fAYpN5bcxlZ+IyIbqfQcoNpXfxlR+IiIbyvgOUGwqv41pnZ+IyIY08osBjfxERNbLoXV+sTAHaPQdQkSkQszOplPrfIcoNpVfoSBsAd7wHUNEpEJEbsoTVH6dmeo7gIhIhYjcxi6g8uvMv30HEBGpEJGcCVP5dUzlJyLivOI7QCmo/DqWAdb4DiEi4lmI1vnFSBC2Aq/6jiEi4tnUbDplfYcoBZVf57TRi4jE3cu+A5SKyq9zWu8nInEXyfV9oPLrispPROJOI78Ymg6s8B1CRMST97Pp1ALfIUpF5deZILTANN8xREQ8ecl3gFJS+XVtiu8AIiKePOM7QCmp/Lr2pO8AIiKePO07QCmp/Lo2FVjiO4SISJnNyqZTM3yHKCWVX1eCMAc85TuGiEiZRXrUByq/nvib7wAiImUW+T/6VX7dexKI5OF9REQ6kAOe9R2i1FR+3QnChcBrvmOIiJTJq9l0aqnvEKWm8usZTX2KSFxEfsoTVH49pfITkbh41HeAclD59cw/gZW+Q4iIlNhC4F++Q5SDyq8ngrCFiB/tQEQE+Gs2ncr5DlEOKr+ei8VUgIjE2kO+A5SLyq/nHgQafYcQESmRkBjNcKn8eioIQ+AR3zFERErkL9l0qsl3iHJR+fXOXb4DiIiUyP2+A5STyq93Hgciv/OniMTOCmKyf18blV9vBGEzMfvrSERi4U9xmvIEld+m0NSniETN73wHKDeVX28F4T+BSJ/nSkRi5Z1sOvWS7xDlpvLbNHf7DiAiUiS3+Q7gg8pv02jqU0SioBn4g+8QPqj8NkUQ/hd42XcMEZE+eiybTi3yHcKHfr4DVLE/AJ/0HUKkmoT/up+17/2L5mVzMf0GMHDzJKMOOpn+Y7bY4HaN8zIsf/5Omj54F0wNA8Zvw/hjLqGm/8AOH3fuTafQunLjz/Bhu6UY85nvALDsmVtY89YzmP6DGHnQSQzb8ZAPb7em/gXWvPUM4796URFfbVX4ve8Avqj8Nt2dwGXACN9BRKpFw/tvMXz3FAMmbge2lRVT/sDC+3/GZt+8iZoBgwBXfAvvv4jEvkcz+rDTMTW1NC2ahTGdT1TVnfQryK0/HnPTktks+uNPGTppPwDWTn+ZNZkpjD/mUlqWz2fpE9cy+KO7UzskQa5xDSue/wMTjvu/0r74yvMBbt/lWNK056YKwlXA7b5jiFSTCcdcwrCdD2PAuK0YMH4bxqTOpnXlYpoWTv/wNsueuZURe3yexD5HM2DcVvQfswVDkwdg+vXv9HFrhySoHTbqw8u66a/Qb2QdAz+yMwDNS99n0Ed2ZmDddgzd4SDMgCG0hAsBWP7cbQzf7Uj6jRhf2hdfeW7NplOtvkP4ovLrm+uBWJz+Q6QUco1rAKgZNAyA1jUraPrgXWqGjmTBnT/i/eu/zoJ7zqNh7ts9fkzb2syadyYz7BOHY4wBYMC4j9K0YDqtDatpXDAd29JIv1Gb0TD3bZoWzmD4Hp8v/ourbI3Ajb5D+KTy64sgnA484TuGSDWyNsfyZ25h4OY7MGDc1gC0rFgAQPjCPQzb5QgmHHMxAyZ8jIX3/YTmZfN69Lhr33uJXMNqhu706Q+XDd5mD4bueDAL7jibpY/9irGps6npP5BlT/6a0UecwapXH2feLaez4K5zaFo8u+ivtQLdm02nFvoO4ZPW+fXdtUDKdwiRarPs7zfRtHg2E0/45YfLrLUADNv1swz7xOEAjJ7wMRpmv87qN59i1EEnd/u4q9/4O4O32YN+w8dssHzk/icwcv8TPvx6xQv3MGjrXTE1tYT/+iObnXIj66a/wtLHrqbu5GuL8Aor2tW+A/imkV9fBeFTwJu+Y4hUk2VP3cS6Gf9mwvG/oN+IsR8urx02CoD+Y7fc4Pb9x3yElpWLu33clnARDbNfZ9guR3R5u+al77PmnecYecDXaZjzJoO22InaIQmGTDqApoUzyDWu3YRXVTWezqZTsf/MUvkVx5W+A4hUA2sty566ibXv/YsJx/2c/iMnbvD9fokJ1A4bTcvSuRssb142r0cbpKx+8ylqhyQY/LG9usyw9MkbGXXoqdQMGAw2h821uG+2XdtIr8qP/agPVH7Fci8wt9tbicTcsqduYvXbkxn7+XOoGTCE1tXLaV29nFxzIwDGGEbs/RVWTnuENfUv0Lx8Piuev5OWZXMZ9onPfPg4C++7gJXTNjy3tLU5Vr/5NEN3+jSmprbTDKtff5LawSMYsq3bTXfg5kkaZr9B47x6Vv77L/Qfs+WHG+BE0DvA33yHqARa51cMQdhMkLgGjQBFurT6Vbdb2cJ7z99g+Zgjz2LYzocBMGKvL2Bbm1j+7K3kGlYxYNxHGX/spfQfVffh7ZuXL2DgupUbPEZD9jVaVy7+cF1hR1rXLCf81/1M/PoVHy4buNn2jNj7Syz608XUDEkwNnV2n19nBbsmm05Z3yEqgWlbwSx9FCSGA+8DCd9RJBoeaDlwyjkt3z7Idw6JjLnAttl0qtF3kEqgac9icTu9x3q/GRGpaD9X8a2n8iuuK4DlvkOIiBSYTQxPWNsVlV8xBeEKIO07hohIgUuz6VSz7xCVROVXfNcDPTsUhYhI6c0A7vAdotKo/IotCNcBge8YIiJ5l2TTqRbfISqNyq80bgPqfYcQkdh7F7jbd4hKpPIrhSBsBX7iO4aIxN7FcT5tUVdUfqUShH8GXvYdQ0Ri6yXgPt8hKpXKr7TO8x1ARGLJAmfqaC6dU/mVUhBORsfRE5HyuyubTr3iO0QlU/mV3vm4v8JERMphNZp16pbKr9SC8DXgFt8xRCQ2LsumU/N9h6h0Kr/yOBdY4DuEiETeLOAq3yGqgcqvHNxhz870HUNEIu8cHby6Z1R+5RKE9wOP+o4hIpH1ZDadetB3iGqh8iuvM4A1vkOISOSsBk7zHaKaqPzKKQjnAD/zHUNEIuf8bDo1x3eIaqLyK7/rgKm+Q4hIZLyATqTdayq/cnPH/TwN0PH2RKSvGoBTdSSX3lP5+RCErwLX+I4hIlXvkmw69a7vENVI5efPhUDWdwgRqVqvAlf4DlGtVH6+BOFa4CQ0/SkivdcEnKKT1G46lZ9PQfg8cInvGCJSdc7NplOv+Q5RzVR+/v0fMNl3CBGpGo8D1/oOUe1Ufr4FYQ44AVjsO4qIVLwFwMnaurPvVH6VIAjn49b/6RdaRDpjgROz6ZT+UC4ClV+lCMIngKt9xxCRinVFNp162neIqFD5VZbzAZ19WUQKvQL81HeIKFH5VZIgbAaOA0LfUUSkYiwHjs+mU82+g0SJyq/SBOEsdHR2EXFageOy6dRM30GiRuVXidy5/37tO4aIeHduNp36u+8QUaTyq1xnAk/5DiEi3tyVTaeu8h0iqlR+lSoIW4CjgXd8RxGRspsGfMt3iChT+VWyIAyBFLDIdxQRKZuFwBez6VSD7yBRpvKrdEGYBb6AO2+XiERbM/DVbDo113eQqFP5VYMgfAkdAUYkDr6VTade8B0iDlR+1cJtAaqdXEWi68JsOnWH7xBxofKrJkH4C+B23zFEpOhuyaZTl/oOEScqv+pzGjoFkkiUPAp8x3eIuFH5VRt3CLQvA2/7jiIiffYP4JhsOtXqO0jcqPyqURAuBz4N1PuOIiKb7A3g89l0ap3vIHGk8qtWQbgQOBR4z3cUEem16cAR2XRKB7H3ROVXzYLwA+AQ3H8kEakO04GDs+nUAt9B4kzlV+3cWeAPAWb4jiIi3Worvnm+g8Sdyi8KgnAurgBn+Y4iIp0qSfEZY84wxmSNMQ3GmJeNMXsX8/GjSuUXFUH4Pq4AZ/uOIiIbKVXxHQtcDVwM7A68DjxpjBlfzOeJIpVflAThbFwBvu87ioh8qJRTnT8AbrHW3matfQf4NrAWOKUEzxUpKr+ocWeCPwTQgXFF/HuXEhWfMWYAsAfwdNsya20u//W+xX6+qFH5RVEQzgD2R+cCFPHpZWD/Em7cMhaoxZ0Cqb2FwMQSPWdkqPyiyk2B7gdM8R1FJIYeBw7NplNLfAeRjqn8oiwIVwBHAPf6jiISI7cDX8imU2tL/DxLgFZgQsHyCYD2IeyGyi/qgrAROAG43HcUkRhIZ9Opb2TTqZZSP5G1tgmYhjvUIQDGmJr81/8q9fNXu36+A0gZBKEFziNIzAGuw60nEDx4EvEAAAaISURBVJHiyQFnZdOp68v8vFcDdxhjpgKvAGcBQ4Hbypyj6mjkFydB+GvgS7hNoUWkONYCx3ooPqy1fwR+BFwCvAbsCnzWWlu4EYwUUPnFTRA+AhwMLPKcRCQKssCnsunUn3wFsNbeYK3dylo70Fr7SWvty76yVBOVXxwF4b9x+wHplEgim+45YK9sOvW67yDSeyq/uArCmcDewB99RxGpQtcDn9GuDNVLG7zEWRCuAo4jSDyPW3E+0HMikUrXCHwnm05pg5Iqp5GftG0I8ylgpu8oIhVsPu5QZSq+CFD5iROE/8EdFf4h31FEKtBjwC7ZdOol30GkODTtKesFYQh8mSBxNm6n+P6eE4n41gicm02nrvUdRIpLIz/ZWBD+CjgQnRpJ4q0e2EfFF00qP+lYEL4E7IY7QK9I3Pwe2DObTr3mO4iUhspPOheES4HPAd8BVntOI1IOIXBcNp36ZjadWuM7jJSOyk+6FoSWIPwNsBPwjO84IiX0V2CHbDqlfV9jQOUnPePOD3g4GgVK9CwGjs+mU1/IplPzfYeR8lD5Sc9tOAp8wncckSK4G0hm06n7fAeR8tKuDtJ7bhR4JEHieOAaYLznRCK9NRf4djadesx3EPFDIz/ZdEF4LzAJ+B1gPacR6YlW4EZgRxVfvGnkJ30ThMuBUwkSd+JGgbt6TiTSmWdxJ5x903cQ8U8jPymOIJwC7AGchHaOl8oyE/hyNp36tIpP2qj8pHiCMEcQ/gH4OHABsNJzIom31bjfwx2y6ZSOWSsb0LSnFF8QNgCXESRuBS4ETkfHCZXyyQF3Audn06kPfIeRyqTyk9IJwsXA/xIkrgPSwJc9J5Jos8CfgSCbTr3lO4xUNpWflF4Q/hf4CkFiP+BKYB/PiSR6/gpcpGNxSk+p/KR8gvBFYF+CxOeBc4H9PCeS6vcEcGE2nZrqO4hUF5WflF8QPgI8QpDYF/gx8AXA+A0lVeYp3EjvX76DSHVS+Yk/Qfgv4EsEie2BHwEnAgP9hpIK1gI8AFyRTade9R1GqpvKT/wLwneBbxEkfgacCXwbGOk3lFSQlbjz612TTadm+w4j0aDyk8oRhAuA8wkSvwBOA84CtvAbSjyaDlwP3JZNp1b5DiPRovKTyhOEq4Cr8rtIHAWcDHwW/b7GQTPwGHAr8EQ2ncp5ziMRpQ8TqVxB2Aw8CDxIkJgIfB1XhDv6jCUl8Q5uavPObDq1yHcYiT6Vn1QHNyV6JXAlQWJPXAl+DRjlM5b0yUrgj8DvsunUy77DSLyo/KT6BOFUYCpB4oesnxY9Aqj1GUt6pBH4O26rzQez6dRaz3kkplR+Ur2CsBH3IfoAQaIO+ArwOeBgtMtEJVmL2xn9T8Bj2nhFKoHKT6IhCD8AbgBuIEgMBQ7HFeGRQJ3PaDG1EngUt872bxrhSaVR+Un0BOEa4GHgYYKEAXbHFeHncOcc1NFkii8HTMMdeeVp4MVsOtXkN5JI51R+Em1BaHEfytOAi/NbjR6J23XiU8DmHtNVuxmsL7tns+nUcs95RHpM5Sfx4rYa/X3+AkFiC2Bf3Jkm9sGNEgf5ilfBmoE3gFeAl4Ep2XQq6zWRSB+o/CTegnAubRvNAASJAcCurC/DfYCP+orn0UxcybWV3avZdKrBbySR4lH5ibQXhE24D/xXgOvcssQEYGdgu4LLNlT3GepzwGwgA9S3u34nm04t8xlMpNRUfiLdCcKFwELcuq12yxO1wNZsXIrbAhOA4eWM2YFWXO55BZdZuKJ7L5tOrfMXT8QflZ/IpgrCVtxGHzOAv238/UR/YDQwpovrMbgzWPTH7aT/4aWBAfOAV3EjtLbLWtxuBKs6uF4FrADm40puQTadai366xaJAGOt9Z1BRESkrGp8BxARESk3lZ+IiMSOyk9ERGJH5SciIrGj8hMRkdhR+YmISOyo/EREJHZUfiIiEjsqPxERiR2Vn4iIxI7KT0REYkflJyIisaPyExGR2FH5iYhI7Kj8REQkdlR+IiISOyo/ERGJHZWfiIjEjspPRERiR+UnIiKxo/ITEZHYUfmJiEjsqPxERCR2VH4iIhI7Kj8REYkdlZ+IiMSOyk9ERGJH5ff/26sDAQAAAABB/taDXBIBsCM/AHbkB8CO/ADYkR8AO/IDYEd+AOzID4Ad+QGwIz8AduQHwI78ANiRHwA78gNgR34A7MgPgB35AbAjPwB25AfAjvwA2JEfADvyA2BHfgDsBG8jcc+PjLFQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "J4ktroLZot_R"
      },
      "outputs": [],
      "source": [
        "# Wordcloud with positive tweets\n",
        "positive_tweets = []\n",
        "for i in conversationDF[conversationDF[\"overall_polarity\"] == 'POS']['comment'].tolist():\n",
        "    positive_tweets.append(re.sub(\"(@[A-Za-z0-9]+)\", \"\", i))\n",
        "\n",
        "if len(positive_tweets) != 0:\n",
        "    stop_words = [\"https\", \"co\", \"RT\"] + list(STOPWORDS)\n",
        "    positive_wordcloud = WordCloud(max_font_size=50, max_words=30, background_color=\"white\", stopwords = stop_words).generate(str(positive_tweets))\n",
        "\n",
        "# Wordcloud with negative tweets\n",
        "negative_tweets = []\n",
        "for i in conversationDF[conversationDF[\"overall_polarity\"] == 'NEG']['comment'].tolist():\n",
        "    negative_tweets.append(re.sub(\"(@[A-Za-z0-9]+)\", \"\", i))\n",
        "\n",
        "if len(negative_tweets) != 0:\n",
        "    stop_words = [\"https\", \"co\", \"RT\"] + list(STOPWORDS)\n",
        "    negative_wordcloud = WordCloud(max_font_size=50, max_words=30, background_color=\"white\", stopwords = stop_words).generate(str(negative_tweets))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "o4fR1OTv9fYR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "ed9c49b4-4ce0-4ee8-c771-23759d5c946d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(6,6), dpi=100)\n",
        "\n",
        "ax1 = fig.add_subplot(211)\n",
        "ax1.title.set_text(\"Positive comments - Wordcloud\")\n",
        "ax1.imshow(positive_wordcloud, interpolation=\"bilinear\")\n",
        "\n",
        "ax2 = fig.add_subplot(212)\n",
        "ax2.title.set_text(\"Negative comments - Wordcloud\")\n",
        "ax2.imshow(negative_wordcloud, interpolation=\"bilinear\")\n",
        "\n",
        "plt.subplots_adjust(left=0.1,\n",
        "                    bottom=0.1, \n",
        "                    right=0.9, \n",
        "                    top=0.9, \n",
        "                    wspace=0.4, \n",
        "                    hspace=0.4)\n",
        "\n",
        "plt.show()\n",
        "fig.savefig(f'{tweets_for_analysis[tweetOption]}/wordcloud_for_positive_and_negative_sentiments.png')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2uEBfLGB-SBI"
      },
      "execution_count": 31,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Main Program (Notebook Version).ipynb",
      "provenance": [],
      "mount_file_id": "1y1bkAU82tZLttQpOvG86q8U2OZssbhi-",
      "authorship_tag": "ABX9TyMIwwVQ8WOVt2oz+kNBlvQ/",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}