newsentimnt.ipynb

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  "metadata": {
    "colab": {
      "name": "newsentimnt.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/playitsaiff/natural-language-processing/blob/master/newsentimnt.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jB1OIVep4tE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IpLP4qrQgR0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC, LinearSVC, NuSVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQ1CFiuNxuXe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        },
        "outputId": "a80123a8-4b58-4463-d7bb-961f5b3e1a32"
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "nltk.download(\"wordnet\")"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sr-dbktSQfxQ",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S16el-cep7hq",
        "colab_type": "code",
        "outputId": "7662d960-c40d-4713-a4a5-fb68c12e8eb0",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 83
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2f8d3813-cfa5-418e-9593-cb1baf17226a\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2f8d3813-cfa5-418e-9593-cb1baf17226a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Sampled.csv to Sampled (4).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96JIOK06p9uh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(\"Sampled.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjhOayhQtKoz",
        "colab_type": "code",
        "outputId": "849f15e7-761e-4d01-bc89-4543c4a583cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "import nltk\n",
        "nltk.download (\"stopwords\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0pNxLRZtand",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#(len(df)-df.count())/len(df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VA_z3Zimtv6U",
        "colab_type": "text"
      },
      "source": [
        "no data place with nan value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQMGmZp-tsn-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df.drop(columns={\"Unnamed: 0\",\"Unnamed: 0.1\"})"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVII74nBvOCO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.airline_name.value_counts()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VFSilPgCXf7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=df.select_dtypes(include=[\"float\", 'int'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KX-Y2V9rDN8D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hLDQemBSCsBT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.join(pd.get_dummies(df[\"cabin_flown\"]))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUBXK6vTDfPn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.join(pd.get_dummies(df[\"airline_name\"]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xct_4o1D3Cq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=X.join(pd.get_dummies(df[\"author_country\"]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_kcykYhvv4j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jdMsDXlEWCs6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['period'] = df[\"Month\"].map(str)+\"-\"+df[\"Year\"].map(str)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxQft2g5bbrC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df[\"Date\"]=pd.to_datetime(df[\"period\"],format=\"%m-%Y\")\n",
        "df=df.drop([\"period\",\"Month\",\"Year\"],axis=1)\n",
        "df.set_index('Date', drop=True, append=False, inplace=True, verify_integrity=False)\n",
        "df = df.sort_index()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U0ev6y85wJ1Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.cabin_flown.value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cduJlylvxCB5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.author_country.value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9Bo2iGfytfc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df.shape\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UJO588A8zYy_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "economy=df[df.cabin_flown==\"Economy\"]\n",
        "business=df[df.cabin_flown==\"Business Class\"]\n",
        "peconomy=df[df.cabin_flown==\"Premium Economy\"]\n",
        "first=df[df.cabin_flown==\"First Class\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZTljfTNK0jEC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#peconomy.shape\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NCPhkX_0lXZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#peconomy.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwDWNJ5dfC6H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "481c25d8-6903-444c-f47e-37b853356e7b"
      },
      "source": [
        "print(len(df.author_country.unique()))\n",
        "print(len(df.author.unique()))\n",
        "print(len(df.airline_name.unique()))\n",
        "print(len(df))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "85\n",
            "1885\n",
            "186\n",
            "2000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wW1OJ26Pf1vv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df.drop_duplicates({\"author\",\"author_country\",\"content\"},keep=\"first\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dFKZ-Gj3hbwX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e3335ed-1ba5-4a73-c83c-13408c18fab9"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUci6g91jcKy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Mood_count=df[\"recommended\"].value_counts()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MSsjO08fjUbv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "cd996c27-ee9d-4c7e-d5da-1aa342aa119e"
      },
      "source": [
        "Index=[0,1]\n",
        "plt.bar(Index,Mood_count)\n",
        "plt.xticks(Index,[\"positive\",\"negative\"],rotation=45)\n",
        "plt.xlabel('Mood')\n",
        "plt.ylabel(\"Mood Count\")\n",
        "plt.title(\"count of Moods\")"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'count of Moods')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JeE079kK6qbjL6abjHjrF55YkTcFkguP7Sd4DnNm2Dwa+P5UnraqvA4vXsmvPtRxbwBFT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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fq0BHv3lXYU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "outputId": "fea9e133-aa14-40c9-f880-9e983c7a3454"
      },
      "source": [
        "\n",
        "df.corr()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>overall_rating</th>\n",
              "      <th>seat_comfort_rating</th>\n",
              "      <th>cabin_staff_rating</th>\n",
              "      <th>food_beverages_rating</th>\n",
              "      <th>inflight_entertainment_rating</th>\n",
              "      <th>value_money_rating</th>\n",
              "      <th>recommended</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>overall_rating</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.726990</td>\n",
              "      <td>0.772878</td>\n",
              "      <td>0.643766</td>\n",
              "      <td>0.411477</td>\n",
              "      <td>0.823172</td>\n",
              "      <td>0.867648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seat_comfort_rating</th>\n",
              "      <td>0.726990</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.602851</td>\n",
              "      <td>0.557131</td>\n",
              "      <td>0.408442</td>\n",
              "      <td>0.706733</td>\n",
              "      <td>0.649986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cabin_staff_rating</th>\n",
              "      <td>0.772878</td>\n",
              "      <td>0.602851</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.650660</td>\n",
              "      <td>0.374240</td>\n",
              "      <td>0.706100</td>\n",
              "      <td>0.694294</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>food_beverages_rating</th>\n",
              "      <td>0.643766</td>\n",
              "      <td>0.557131</td>\n",
              "      <td>0.650660</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.497740</td>\n",
              "      <td>0.609965</td>\n",
              "      <td>0.574535</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>inflight_entertainment_rating</th>\n",
              "      <td>0.411477</td>\n",
              "      <td>0.408442</td>\n",
              "      <td>0.374240</td>\n",
              "      <td>0.497740</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.405268</td>\n",
              "      <td>0.358090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>value_money_rating</th>\n",
              "      <td>0.823172</td>\n",
              "      <td>0.706733</td>\n",
              "      <td>0.706100</td>\n",
              "      <td>0.609965</td>\n",
              "      <td>0.405268</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.755641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>recommended</th>\n",
              "      <td>0.867648</td>\n",
              "      <td>0.649986</td>\n",
              "      <td>0.694294</td>\n",
              "      <td>0.574535</td>\n",
              "      <td>0.358090</td>\n",
              "      <td>0.755641</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               overall_rating  ...  recommended\n",
              "overall_rating                       1.000000  ...     0.867648\n",
              "seat_comfort_rating                  0.726990  ...     0.649986\n",
              "cabin_staff_rating                   0.772878  ...     0.694294\n",
              "food_beverages_rating                0.643766  ...     0.574535\n",
              "inflight_entertainment_rating        0.411477  ...     0.358090\n",
              "value_money_rating                   0.823172  ...     0.755641\n",
              "recommended                          0.867648  ...     1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u0yA6vDRlrh1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WHTls1-u-48P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_rating=X[\"recommended\"]\n",
        "y_rating_train=y_rating[:1500][:]\n",
        "y_rating_test=y_rating[1500:][:]\n",
        "x_rating=X.drop(columns=\"recommended\")\n",
        "x_rating_train=x_rating[:1500][:]\n",
        "x_rating_test=x_rating[1500:][:]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rufFYEPWQB7X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Classifiers = [\n",
        "    LogisticRegression(C=0.000001,solver='liblinear',max_iter=200),\n",
        "    KNeighborsClassifier(3),\n",
        "    SVC(kernel=\"rbf\", C=0.025, probability=True),\n",
        "    DecisionTreeClassifier(),\n",
        "    RandomForestClassifier(n_estimators=200),\n",
        "    AdaBoostClassifier(),\n",
        "    GaussianNB()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRZxQsABl3r2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "outputId": "9140b158-1c8c-4382-984c-564021a849d2"
      },
      "source": [
        "\n",
        "Accuracy=[]\n",
        "Model=[]\n",
        "for classifier in Classifiers:\n",
        "    fit = classifier.fit(x_rating_train,y_rating_train)\n",
        "    pred = fit.predict(x_rating_test)\n",
        "    accuracy = accuracy_score(pred,y_rating_test)\n",
        "    Accuracy.append(accuracy)\n",
        "    Model.append(classifier.__class__.__name__)\n",
        "    print('Accuracy of '+classifier.__class__.__name__+'is '+str(accuracy))  "
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of LogisticRegressionis 0.616\n",
            "Accuracy of KNeighborsClassifieris 0.924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
            "  \"avoid this warning.\", FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy of SVCis 0.926\n",
            "Accuracy of DecisionTreeClassifieris 0.926\n",
            "Accuracy of RandomForestClassifieris 0.94\n",
            "Accuracy of AdaBoostClassifieris 0.93\n",
            "Accuracy of GaussianNBis 0.47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wULvcMg0l6Fk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "outputId": "f2360857-932e-4212-cf53-570357e76356"
      },
      "source": [
        "Index = [1,2,3,4,5,6,7]\n",
        "plt.bar(Index,Accuracy)\n",
        "plt.xticks(Index, Model,rotation=90)\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Model')\n",
        "plt.title('Accuracies of Models')"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracies of Models')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EYH/gV33GEhERAzJhUpC0vqT3Svo98DlKDSTZfpHtwyd6XZfnAlfbnlvNgj4R2Hmc530A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\nERG1JIWIiKj9f+PdHyGpkpl5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AYCpj2xwMtE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_review[\"text\"]=df[\"content\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEFkH7VowTRJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re,nltk\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords\n",
        "stop_words=set(stopwords.words(\"english\"))\n",
        "wordnet_lemmatizer=WordNetLemmatizer()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OHK7T-SvwUT7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def normalizer(review):\n",
        "    only_letters = re.sub(\"[^a-zA-Z]\", \" \",review) \n",
        "    tokens = nltk.word_tokenize(only_letters)[2:]\n",
        "    lower_case = [l.lower() for l in tokens]\n",
        "    filtered_result = list(filter(lambda l: l not in stop_words, lower_case))\n",
        "    lemmas = [wordnet_lemmatizer.lemmatize(t) for t in filtered_result]\n",
        "    return lemmas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8TtDPJ8xqcI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "b5ab79d5-31ec-4411-c733-e2f1a926980f"
      },
      "source": [
        "pd.set_option('display.max_colwidth', -1)\n",
        "x_review['normalized'] = x_review.apply(normalizer)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-83-aba6116d2b4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'display.max_colwidth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx_review\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'normalized'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_review\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalizer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3590\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3593\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-70-88064d7631ce>\u001b[0m in \u001b[0;36mnormalizer\u001b[0;34m(review)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnormalizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreview\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0monly_letters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[^a-zA-Z]\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mreview\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_tokenize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monly_letters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mlower_case\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtokens\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mfiltered_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstop_words\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlower_case\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/re.py\u001b[0m in \u001b[0;36msub\u001b[0;34m(pattern, repl, string, count, flags)\u001b[0m\n\u001b[1;32m    189\u001b[0m     \u001b[0ma\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mit\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mobject\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mmust\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     a replacement string to be used.\"\"\"\n\u001b[0;32m--> 191\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_compile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msubn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpattern\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: expected string or bytes-like object"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJXzQXhPzLFc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk import ngrams\n",
        "def ngrams(input_list):\n",
        "    #onegrams = input_list\n",
        "    bigrams = [' '.join(t) for t in list(zip(input_list, input_list[1:]))]\n",
        "    trigrams = [' '.join(t) for t in list(zip(input_list, input_list[1:], input_list[2:]))]\n",
        "    return bigrams+trigrams\n",
        "x_review['grams'] = tweets.normalized_tweet.apply(ngrams)\n",
        "tweets[['grams']].head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMVnQGXizghh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "477babfd-d873-4acb-88ce-be8ad20eefb7"
      },
      "source": [
        "x_review.columns\n",
        "\n"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-86-c5d03c64a259>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx_review\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5061\u001b[0m         if (name in self._internal_names_set or name in self._metadata or\n\u001b[1;32m   5062\u001b[0m                 name in self._accessors):\n\u001b[0;32m-> 5063\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5064\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Series' object has no attribute 'columns'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTph88JgJ29u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}