British_Airways_analysis (google colab).ipynb

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   "id": "dc6cc421",
   "metadata": {
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   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "HkFduc33-iLi",
   "metadata": {
    "colab": {
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       "'/content'"
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     "output_type": "execute_result"
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   ],
   "source": [
    "import os\n",
    "\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb83be5f",
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   "source": [
    "df = pd.read_csv(\"/BA_reviews.csv\")"
   ]
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  {
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   "id": "eddc6adb",
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    "id": "eddc6adb"
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   "source": [
    "## Data cleaning"
   ]
  },
  {
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       "      <td>✅ Trip Verified |  Outward journey BA245 Londo...</td>\n",
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       "      <th>999</th>\n",
       "      <td>999</td>\n",
       "      <td>✅ Trip Verified |  Paid for a Vueling Airlines...</td>\n",
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      "text/plain": [
       "     Unnamed: 0                                            reviews\n",
       "0             0  ✅ Trip Verified | BA 242 on the 6/2/23. Boardi...\n",
       "1             1  ✅ Trip Verified |  Not only my first flight in...\n",
       "2             2  ✅ Trip Verified |  My husband and myself were ...\n",
       "3             3  ✅ Trip Verified | Organised boarding process. ...\n",
       "4             4  ✅ Trip Verified |  Outward journey BA245 Londo...\n",
       "..          ...                                                ...\n",
       "995         995  ✅ Trip Verified | Madrid to London. Credit whe...\n",
       "996         996  ✅ Trip Verified | Venice to Gatwick. I use Sno...\n",
       "997         997  ✅ Trip Verified | First 3 legs were trouble fr...\n",
       "998         998  ✅ Trip Verified | Flew London to Budapest with...\n",
       "999         999  ✅ Trip Verified |  Paid for a Vueling Airlines...\n",
       "\n",
       "[1000 rows x 2 columns]"
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   "source": [
    "df.drop(\"Unnamed: 0\", axis = 1, inplace= True)"
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       "                                               reviews\n",
       "0    ✅ Trip Verified | BA 242 on the 6/2/23. Boardi...\n",
       "1    ✅ Trip Verified |  Not only my first flight in...\n",
       "2    ✅ Trip Verified |  My husband and myself were ...\n",
       "3    ✅ Trip Verified | Organised boarding process. ...\n",
       "4    ✅ Trip Verified |  Outward journey BA245 Londo...\n",
       "..                                                 ...\n",
       "995  ✅ Trip Verified | Madrid to London. Credit whe...\n",
       "996  ✅ Trip Verified | Venice to Gatwick. I use Sno...\n",
       "997  ✅ Trip Verified | First 3 legs were trouble fr...\n",
       "998  ✅ Trip Verified | Flew London to Budapest with...\n",
       "999  ✅ Trip Verified |  Paid for a Vueling Airlines...\n",
       "\n",
       "[1000 rows x 1 columns]"
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   "source": [
    "df = df[\"reviews\"].str.split(\"|\", expand=True)"
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   "execution_count": 13,
   "id": "eb2aff03",
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   "outputs": [],
   "source": [
    "df = df.rename(columns={0:\"Verification\", 1: \"Comment\"})"
   ]
  },
  {
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       "      <th>4</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Outward journey BA245 London to Buenos Aires...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Madrid to London. Credit where it's due. Flew...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Venice to Gatwick. I use Snokart luggage whic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>First 3 legs were trouble free. Lounges were ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Flew London to Budapest with British Airways....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Paid for a Vueling Airlines flight from Flor...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "         Verification                                            Comment\n",
       "0    ✅ Trip Verified    BA 242 on the 6/2/23. Boarding was delayed du...\n",
       "1    ✅ Trip Verified     Not only my first flight in 17 years, but al...\n",
       "2    ✅ Trip Verified     My husband and myself were flying to Madrid ...\n",
       "3    ✅ Trip Verified    Organised boarding process. Really friendly c...\n",
       "4    ✅ Trip Verified     Outward journey BA245 London to Buenos Aires...\n",
       "..                ...                                                ...\n",
       "995  ✅ Trip Verified    Madrid to London. Credit where it's due. Flew...\n",
       "996  ✅ Trip Verified    Venice to Gatwick. I use Snokart luggage whic...\n",
       "997  ✅ Trip Verified    First 3 legs were trouble free. Lounges were ...\n",
       "998  ✅ Trip Verified    Flew London to Budapest with British Airways....\n",
       "999  ✅ Trip Verified     Paid for a Vueling Airlines flight from Flor...\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
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    "df"
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       "   Id      Verification                                            Comment\n",
       "0   0  ✅ Trip Verified    BA 242 on the 6/2/23. Boarding was delayed du...\n",
       "1   1  ✅ Trip Verified     Not only my first flight in 17 years, but al...\n",
       "2   2  ✅ Trip Verified     My husband and myself were flying to Madrid ...\n",
       "3   3  ✅ Trip Verified    Organised boarding process. Really friendly c...\n",
       "4   4  ✅ Trip Verified     Outward journey BA245 London to Buenos Aires..."
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    "df = df.reset_index().rename(columns = {\"index\":\"Id\"})\n",
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   "source": [
    "## Trips that are not verified"
   ]
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       "      <th>950</th>\n",
       "      <td>950</td>\n",
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       "<p>171 rows × 3 columns</p>\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-c670f3e5-b080-460a-a264-0efbcc4afa14');\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",
       "  "
      ],
      "text/plain": [
       "      Id   Verification                                            Comment\n",
       "11    11  Not Verified    It seems that there is a race to the bottom a...\n",
       "12    12  Not Verified     As a Spanish born individual living in Engla...\n",
       "16    16  Not Verified     I find BA incredibly tacky and constantly lo...\n",
       "18    18  Not Verified     Great thing about British Airways A380 is th...\n",
       "19    19  Not Verified    The staff are friendly. The plane was cold, w...\n",
       "..   ...            ...                                                ...\n",
       "868  868  Not Verified     Glasgow to Miami via London. Glasgow to Heat...\n",
       "877  877  Not Verified     London Heathrow to Düsseldorf. Really good c...\n",
       "945  945  Not Verified     Los Angeles to London. I booked with AA, LAX...\n",
       "946  946  Not Verified     The overall flight wasn't too bad. Seats and...\n",
       "950  950  Not Verified     First time flying with British Airways in ec...\n",
       "\n",
       "[171 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Verification'].str.contains(\"Not\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3560020a",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "id": "3560020a",
    "outputId": "ec275025-beb5-4349-b6ac-95b985a9aa65"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "' It seems that there is a race to the bottom amongst airlines. I cannot even check in because BA requires the submission of documents for Covid restrictions. However the form to start the submission process is hopelessly broken. After clicking \"submit\" the form just hangs forever. No error message is given. I tried three different browsers on three different devices. All have the same hang. Which means you cannot check in online nor can you select a seat. I tried using the chat bot, but after 30 minutes of clicking \"try again\" I thought I had donated enough time to BA. '"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Comment\"][11]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7904a45b",
   "metadata": {
    "id": "7904a45b"
   },
   "source": [
    "## VADER Seniment Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e6c32a5",
   "metadata": {
    "id": "2e6c32a5"
   },
   "outputs": [],
   "source": [
    "from nltk.sentiment import SentimentIntensityAnalyzer\n",
    "from tqdm.notebook import tqdm #Progress bar "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bN9115m_lQYk",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bN9115m_lQYk",
    "outputId": "35fc597f-14c5-4156-e3b7-70e023f23390"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " import nltk\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37352066",
   "metadata": {
    "id": "37352066"
   },
   "outputs": [],
   "source": [
    "sia = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90605b28",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "90605b28",
    "outputId": "b8f79b7d-de64-4349-fedd-c3603ee3bc92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'neg': 0.078, 'neu': 0.922, 'pos': 0.0, 'compound': -0.6808}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sia.polarity_scores(df[\"Comment\"][11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df042516",
   "metadata": {
    "id": "df042516"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "76b4cd5b",
   "metadata": {
    "id": "76b4cd5b"
   },
   "source": [
    "## Roberta model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ocxNi8lClg0T",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ocxNi8lClg0T",
    "outputId": "e4cd9609-1a5d-4ca1-fbaa-802cd7ca1a6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting transformers\n",
      "  Downloading transformers-4.29.1-py3-none-any.whl (7.1 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m73.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
      "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
      "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m26.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
      "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
      "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m106.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
      "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.4.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
      "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
      "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.29.1\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f6b3939f",
   "metadata": {
    "id": "f6b3939f"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForSequenceClassification\n",
    "from scipy.special import softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d53e89d",
   "metadata": {},
   "source": [
    "# Do not run this code, it requires GPU and that is not available on this local machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "43656bad",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 177,
     "referenced_widgets": [
      "f722685362514008ace4047804922860",
      "0aca80b392f7489b921ace54ce9bd177",
      "2172fd988b864252bd411f8384a8c9a2",
      "d6f04e2fdd7d48199be51101a344d3c9",
      "c9a3da21b4434858be40cdb333c8b0ba",
      "e21fccbd2c534408a55294d51023429c",
      "eafd4bd88fa74725be7542d00890b798",
      "f8a4c1f59c1248eba9acec6f41938e32",
      "69c43769a5d246c2be076dd19bdef4d4",
      "d769bff328ff4461a455fe8c3c820f4c",
      "10de8be6fc454a2c84604f832f0ef160",
      "fe7666ebc9554b56a8fbb300e8459f0c",
      "5a5626a168ef4efbb019b9e645be1efa",
      "fa8ec5df4cb144468b31af7ddfae078a",
      "fea7878540684111bc206205c22955b1",
      "2e7562b2a1a14a3c8df9e7353a5e4831",
      "0195473d902e4759a2b7de88f56797f4",
      "090f0ccc16904b88bf3f2560e866a396",
      "e35ec824e9914780ad86e8cae3515265",
      "89f588f675514b19b94978ea499bb3e8",
      "c55cf208f15746ac80ca0c3bb3c3b282",
      "6268396493374e34a5b0b1d721c51fe4",
      "87962d260f574f10b6a947e2770d33ee",
      "0023072cd73f4e088edf92f75544b3f4",
      "9fb096e0720a47f7ac7eaeaf6a711455",
      "e24f5242ebbc4bdfbba7a5230f337ec6",
      "084149a23a594d309f715dd5d90beb0c",
      "e6d08792da87444bb8a5e71ece3dd5fa",
      "b27c2db44d94439f9ad3eebd0424ee90",
      "ec9ba7381aa9488580ff06cd539d378c",
      "7b51561404224d26a2f6942ac24fee82",
      "6de2de0c7e9049a7b0c9563dc966a25e",
      "a611f7d32a6148b497dc3bab082cb5b3",
      "b9f4aea73b7443509c994bebc92ba10f",
      "b8ec3a1c9b2d4c628351e38fcf6e8985",
      "b6784e93dc9f4ccb9d233eb4aa8269c8",
      "43e1ef13b3334ecbb65a3551003af125",
      "1b7a3582494b4f1eb92a3beb793baaa4",
      "a9cb2185d4ba469e91331b4593891b11",
      "8f1c8066698d49eaa7c3e58af0881088",
      "61ecacb9e57e4f3b8d1fc3ea1f659db6",
      "81f6c208dbc846f196b65fafacb3a91d",
      "45be43b3032f428596097b1f930def27",
      "45973a3b5fd449a6a41eb2681a110151",
      "505e8a297c1b42448774d7cdb1501b1c",
      "165fd9fc25464fc0996da4a4821ef1b4",
      "5011f9a20229440e834aa66c9aee4413",
      "1e24dab7c18b43a2b54ab05fa37a9593",
      "4cb967765fcc4efc914918df8db65b0a",
      "358c6fbcbf8149aa9800f9f55f76fdef",
      "ace201163b66442d92f6176c7f61b719",
      "c89d3f0e5d93432681d1e4ca796c492b",
      "1afd6517e185424c9d307158133ad65a",
      "873ce63110b34a188c868ab8e08dc671",
      "ad1ac28eb3c6472d82f586531863d0ee"
     ]
    },
    "id": "43656bad",
    "outputId": "38378155-7341-4615-eec4-e7e245ff380f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f722685362514008ace4047804922860",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)lve/main/config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe7666ebc9554b56a8fbb300e8459f0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87962d260f574f10b6a947e2770d33ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9f4aea73b7443509c994bebc92ba10f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "505e8a297c1b42448774d7cdb1501b1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading pytorch_model.bin:   0%|          | 0.00/499M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MODEL = f\"cardiffnlp/twitter-roberta-base-sentiment\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
    "model = AutoModelForSequenceClassification.from_pretrained(MODEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "afa8caa0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "afa8caa0",
    "outputId": "34aaf257-0695-4bf5-c01f-ff7a1e25bc6e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'roberta_neg': 0.9187368, 'roberta_neu': 0.07319002, 'roberta_pos': 0.008073245}\n"
     ]
    }
   ],
   "source": [
    "# Run for Roberta Model\n",
    "encoded_text = tokenizer(df[\"Comment\"][11], return_tensors='pt') # Encoded text\n",
    "output = model(**encoded_text) # Running model on encoded text\n",
    "scores = output[0][0].detach().numpy() # Converting tensor output to numpy\n",
    "scores = softmax(scores)\n",
    "scores_dict = {\n",
    "    'roberta_neg' : scores[0],\n",
    "    'roberta_neu' : scores[1],\n",
    "    'roberta_pos' : scores[2]\n",
    "}\n",
    "print(scores_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f1933fb4",
   "metadata": {
    "id": "f1933fb4"
   },
   "outputs": [],
   "source": [
    "def polarity_scores_roberta(example):\n",
    "    encoded_text = tokenizer(example, return_tensors='pt')\n",
    "    output = model(**encoded_text)\n",
    "    scores = output[0][0].detach().numpy()\n",
    "    scores = softmax(scores)\n",
    "    scores_dict = {\n",
    "        'roberta_neg' : scores[0],\n",
    "        'roberta_neu' : scores[1],\n",
    "        'roberta_pos' : scores[2]\n",
    "    }\n",
    "    return scores_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9b8668",
   "metadata": {},
   "source": [
    "# Only run this code when using Google colab GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6687a04",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 500,
     "referenced_widgets": [
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      "0e6dc2a74d4a4e8d97c168f95f00353a",
      "c87006608aff41bab5390d1c6f6c2c1e",
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      "37ad319b7116449ea7e7395fbb6035a8",
      "6abec22d96fe49a199062a36000c0b57"
     ]
    },
    "id": "b6687a04",
    "outputId": "74d41403-6558-46f6-841e-41a383b6fd1c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "62047cc7379c494a87dc8f7a87fffa9d",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Broke for id 20\n",
      "Broke for id 28\n",
      "Broke for id 106\n",
      "Broke for id 126\n",
      "Broke for id 191\n",
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      "Broke for id 534\n",
      "Broke for id 558\n",
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      "Broke for id 691\n",
      "Broke for id 715\n",
      "Broke for id 779\n",
      "Broke for id 791\n",
      "Broke for id 847\n",
      "Broke for id 862\n",
      "Broke for id 879\n",
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      "Broke for id 889\n",
      "Broke for id 900\n",
      "Broke for id 964\n",
      "Broke for id 970\n",
      "Broke for id 971\n"
     ]
    }
   ],
   "source": [
    "res = {}\n",
    "for i, row in tqdm(df.iterrows(), total=len(df)):\n",
    "    try:\n",
    "        text = row['Comment']\n",
    "        myid = row['Id']\n",
    "        vader_result = sia.polarity_scores(text)\n",
    "        vader_result_rename = {}\n",
    "        for key, value in vader_result.items():\n",
    "            vader_result_rename[f\"vader_{key}\"] = value # Rename keys to vader_neg, vader_pos, vader_neu\n",
    "        roberta_result = polarity_scores_roberta(text)\n",
    "        both = {**vader_result_rename, **roberta_result} # combining two dictionaries\n",
    "        res[myid] = both #???\n",
    "    except RuntimeError:\n",
    "        print(f'Broke for id {myid}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b7105e88",
   "metadata": {
    "id": "b7105e88"
   },
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame(res).T\n",
    "results_df = results_df.reset_index().rename(columns={'index': 'Id'})\n",
    "results_df = results_df.merge(df, how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aaWiBVJJDWRk",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "aaWiBVJJDWRk",
    "outputId": "2a4c722c-3ee2-4e48-83f8-7d89cdd23aaf"
   },
   "outputs": [
    {
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       "      <th>Id</th>\n",
       "      <th>vader_neg</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.005097</td>\n",
       "      <td>0.038937</td>\n",
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       "      <td>✅ Trip Verified</td>\n",
       "      <td>BA 242 on the 6/2/23. Boarding was delayed du...</td>\n",
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       "      <td>0.053335</td>\n",
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       "      <td>✅ Trip Verified</td>\n",
       "      <td>Not only my first flight in 17 years, but al...</td>\n",
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       "      <td>0.023</td>\n",
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       "      <td>0.168</td>\n",
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       "      <td>0.171544</td>\n",
       "      <td>0.458566</td>\n",
       "      <td>0.369891</td>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>My husband and myself were flying to Madrid ...</td>\n",
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       "      <td>0.011498</td>\n",
       "      <td>0.986977</td>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Organised boarding process. Really friendly c...</td>\n",
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       "      <td>0.257831</td>\n",
       "      <td>0.040269</td>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Outward journey BA245 London to Buenos Aires...</td>\n",
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      "text/plain": [
       "   Id  vader_neg  vader_neu  vader_pos  vader_compound  roberta_neg  \\\n",
       "0   0      0.032      0.650      0.318          0.9564     0.005097   \n",
       "1   1      0.023      0.788      0.189          0.9930     0.007140   \n",
       "2   2      0.023      0.809      0.168          0.9933     0.171544   \n",
       "3   3      0.000      0.605      0.395          0.9371     0.001525   \n",
       "4   4      0.095      0.817      0.088         -0.0980     0.701900   \n",
       "\n",
       "   roberta_neu  roberta_pos      Verification  \\\n",
       "0     0.038937     0.955966  ✅ Trip Verified    \n",
       "1     0.053335     0.939525  ✅ Trip Verified    \n",
       "2     0.458566     0.369891  ✅ Trip Verified    \n",
       "3     0.011498     0.986977  ✅ Trip Verified    \n",
       "4     0.257831     0.040269  ✅ Trip Verified    \n",
       "\n",
       "                                             Comment  \n",
       "0   BA 242 on the 6/2/23. Boarding was delayed du...  \n",
       "1    Not only my first flight in 17 years, but al...  \n",
       "2    My husband and myself were flying to Madrid ...  \n",
       "3   Organised boarding process. Really friendly c...  \n",
       "4    Outward journey BA245 London to Buenos Aires...  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "DxPaSiB5DX8c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "id": "DxPaSiB5DX8c",
    "outputId": "c7bcea3b-3bb9-4822-a230-5cdf5664c66b"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'  I find BA incredibly tacky and constantly looking for ways to get money out of customers. I have travelled using major airlines my whole life and although I know it sounds silly - when on a 4 hour flight on a major airline you do expect some form of beverage or snack as a curtesy and a port to charge your phone and not have to pay insane prices for luggage. There was no inflight entertainment and the aircraft was very snug, when I challenged why I had to pay for my own drink and why no inflight entertainment - I was told it was a short haul fight. My sisters met me from Cairo (which is an hour and half flight) and flew Egyptair and somehow received luggage allowance, drinks, food and inflight entertainment as well as a USB port to charge their phones. BA I now realise stands for Budget Airline. Staff were great - what you got for your money not so much.'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[\"Comment\"][16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "kw_iV4UtDYXt",
   "metadata": {
    "id": "kw_iV4UtDYXt"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "GVaFUNVUCkOs",
   "metadata": {
    "id": "GVaFUNVUCkOs"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "Vg_Ab1o5Bxkn",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "Vg_Ab1o5Bxkn",
    "outputId": "fadaabc1-6365-49f7-991c-20db478d5e29"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=vector, **plot_kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=vector, **plot_kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1507: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=vector, **plot_kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n",
      "/usr/local/lib/python3.10/dist-packages/seaborn/axisgrid.py:1609: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  func(x=x, y=y, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=results_df,\n",
    "             vars=['roberta_neg', 'roberta_neu', 'roberta_pos'],\n",
    "            palette='tab10');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5qva50QRM_R9",
   "metadata": {
    "id": "5qva50QRM_R9"
   },
   "outputs": [],
   "source": [
    "results_df['Max'] = results_df[['roberta_neg', 'roberta_neu', 'roberta_pos']].idxmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "_q-E0HSWN-D2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 190
    },
    "id": "_q-E0HSWN-D2",
    "outputId": "5d2b1a90-333a-49ac-829f-fa31fcb5d70e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-5eda9cda-9e96-4cba-9636-4cb50c738d62\">\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>vader_neg</th>\n",
       "      <th>vader_neu</th>\n",
       "      <th>vader_pos</th>\n",
       "      <th>vader_compound</th>\n",
       "      <th>roberta_neg</th>\n",
       "      <th>roberta_neu</th>\n",
       "      <th>roberta_pos</th>\n",
       "      <th>Verification</th>\n",
       "      <th>Comment</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0.9564</td>\n",
       "      <td>0.005097</td>\n",
       "      <td>0.038937</td>\n",
       "      <td>0.955966</td>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>BA 242 on the 6/2/23. Boarding was delayed du...</td>\n",
       "      <td>roberta_pos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.023</td>\n",
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       "      <td>0.007140</td>\n",
       "      <td>0.053335</td>\n",
       "      <td>0.939525</td>\n",
       "      <td>✅ Trip Verified</td>\n",
       "      <td>Not only my first flight in 17 years, but al...</td>\n",
       "      <td>roberta_pos</td>\n",
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       "    }\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-5eda9cda-9e96-4cba-9636-4cb50c738d62 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-5eda9cda-9e96-4cba-9636-4cb50c738d62');\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",
       "  "
      ],
      "text/plain": [
       "   Id  vader_neg  vader_neu  vader_pos  vader_compound  roberta_neg  \\\n",
       "0   0      0.032      0.650      0.318          0.9564     0.005097   \n",
       "1   1      0.023      0.788      0.189          0.9930     0.007140   \n",
       "\n",
       "   roberta_neu  roberta_pos      Verification  \\\n",
       "0     0.038937     0.955966  ✅ Trip Verified    \n",
       "1     0.053335     0.939525  ✅ Trip Verified    \n",
       "\n",
       "                                             Comment          Max  \n",
       "0   BA 242 on the 6/2/23. Boarding was delayed du...  roberta_pos  \n",
       "1    Not only my first flight in 17 years, but al...  roberta_pos  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85YnkB0iP7rO",
   "metadata": {
    "id": "85YnkB0iP7rO"
   },
   "outputs": [],
   "source": [
    "results_df['Max'].replace({'roberta_neg': 'Negative', 'roberta_pos': 'Positive', 'roberta_neu': 'Neutral'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "hIWFCB6rOCPm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 537
    },
    "id": "hIWFCB6rOCPm",
    "outputId": "3052cfb1-7ee3-4f63-fa18-3459fc1e4ae6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Histogram Of Sentiment Frequency'}, xlabel='Sentiments'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df[\"Max\"].value_counts().plot(kind = \"bar\", title = \"Histogram Of Sentiment Frequency\", xlabel = \"Sentiments\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ZFvJNdgqOH82",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZFvJNdgqOH82",
    "outputId": "fadba473-e6c7-4927-fddd-6dc60b1a2e1a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Negative    0.586242\n",
       "Positive    0.328542\n",
       "Neutral     0.085216\n",
       "Name: Max, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[\"Max\"].value_counts(normalize = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "yMvoyeW7OmCl",
   "metadata": {
    "id": "yMvoyeW7OmCl"
   },
   "outputs": [],
   "source": [
    "tokens = ' '.join(results_df[\"Comment\"]).split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bGNIRnhfXahX",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bGNIRnhfXahX",
    "outputId": "856fa088-3b8d-4230-ae6d-c46095265f26"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "135543"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "LM9XT_KXX6eF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LM9XT_KXX6eF",
    "outputId": "dae0867d-4a3b-46b7-f1c5-41a4e355286b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['', 'BA', '242', 'on', 'the', '6/2/23.', 'Boarding', 'was', 'delayed', 'due']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "tTvXjpdJofZh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tTvXjpdJofZh",
    "outputId": "9c2abbae-9e25-4431-8153-3518b65cb8c8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "OqpW_5HGXdAi",
   "metadata": {
    "id": "OqpW_5HGXdAi"
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "stop_words = stopwords.words(\"english\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "gyZBju_GYfCp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gyZBju_GYfCp",
    "outputId": "53b1734f-6171-44af-a2dd-388b6cea5837"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80967"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens_no_sw = [item for item in tokens if item not in stop_words] # Remove stop words from token\n",
    "len(tokens_no_sw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "XZAIeSWgT7MN",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XZAIeSWgT7MN",
    "outputId": "cb981863-111a-40a5-d997-1f92516d3a4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "69"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens_no_sw.count(\"help\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "kbvl4nOUUIF9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "dad33c065ad34f24b72b6b69aea6e5bf",
      "9acce7bc96a34d0d965650b2d8b7e9f6",
      "7682652cdbd94e1c8c301f6a88bd27a0",
      "0bf80188c0b84a428bc245668a1272d9",
      "f2f491b3a51f442f87cec1765434c3ce",
      "3b827aec76d14145976df819758d6ee4",
      "78ef971220b8471c9ff38168807f8632",
      "b068f2a744114f39ba03da7357cd3e55",
      "7a1cc779085846f9806908d8d1af70de",
      "5a3c7c6386ae4a47addc706aef9e9a67",
      "4e17bef77ff142049caee60dd5248fea"
     ]
    },
    "id": "kbvl4nOUUIF9",
    "outputId": "b53d2579-4928-48bd-9e4c-23153226d6fb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dad33c065ad34f24b72b6b69aea6e5bf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/80967 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tokens_dict = {}\n",
    "for w in tqdm(tokens_no_sw):\n",
    "  tokens_dict[w] = tokens_no_sw.count(w)/len(tokens_no_sw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "zF2eefZlZ66x",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zF2eefZlZ66x",
    "outputId": "2e21cc92-19ea-4478-93a4-cd62f2cd12a0"
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "\n",
    "keys = list(tokens_dict.keys())\n",
    "values = list(tokens_dict.values())\n",
    "sorted_value_index = np.argsort(values)\n",
    "sorted_dict = {keys[i]: values[i] for i in sorted_value_index}\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "m5GkARsdpOLT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m5GkARsdpOLT",
    "outputId": "15ea08f3-7612-4b2e-9183-eba4a818178b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x7f94d06ca140>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = list(sorted_dict.keys())\n",
    "freq = list(sorted_dict.values())\n",
    "zip(words, freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "vmLCx5Oprjmm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vmLCx5Oprjmm",
    "outputId": "2ed8a8df-ed00-4399-e96b-db3290e77376"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['back',\n",
       " 'could',\n",
       " 'hours',\n",
       " 'Heathrow',\n",
       " 'us',\n",
       " 'Airways',\n",
       " 'We',\n",
       " 'class',\n",
       " 'flight.',\n",
       " 'business',\n",
       " 'get',\n",
       " 'seats',\n",
       " 'good',\n",
       " 'staff',\n",
       " 'time',\n",
       " 'cabin',\n",
       " 'one',\n",
       " 'would',\n",
       " '-',\n",
       " 'British',\n",
       " 'food',\n",
       " 'crew',\n",
       " 'seat',\n",
       " 'London',\n",
       " 'service',\n",
       " 'BA',\n",
       " 'The',\n",
       " 'flight',\n",
       " '',\n",
       " 'I']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words = words[-30:] # Most frequent 30 words\n",
    "freq = freq[-30:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "KMn4ho9Et8aw",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KMn4ho9Et8aw",
    "outputId": "0b863582-299c-4622-ae47-8aebdb1c739f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The\n",
      "I\n",
      "Length of words list is 21, Length of frequency list is 21\n"
     ]
    }
   ],
   "source": [
    "for i,j in zip(words, freq):\n",
    "  if len(i)<4:\n",
    "    print(i)\n",
    "    words.remove(i)\n",
    "    freq.remove(j)\n",
    "print(f\"Length of words list is {len(words)}, Length of frequency list is {len(freq)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "61EepblRwkWh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "61EepblRwkWh",
    "outputId": "66fd4e1b-d992-4cbf-da54-fb692f8499cf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "AIiOrgoYs0sB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 505
    },
    "id": "AIiOrgoYs0sB",
    "outputId": "98827a83-344c-42be-fadb-4a341f766ead"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(words)), freq, tick_label = words)\n",
    "plt.title(\"Histogram of Most Frequently Used Words\")\n",
    "plt.xticks(rotation=90);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f0OjtJRfpPFp",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f0OjtJRfpPFp",
    "outputId": "9aab9334-eb88-41c7-d307-234f7818741e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 0]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = [1,2,3,4,5,6,7,8,9,0]\n",
    "test[-5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fTpCr6lJpP6K",
   "metadata": {
    "id": "fTpCr6lJpP6K"
   },
   "outputs": [],
   "source": []
  },
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   "metadata": {
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   },
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   "source": []
  }
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