data_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "16dbd996-bfcf-40e4-834e-be346ceb6cb5",
   "metadata": {
    "tags": []
   },
   "source": [
    "!python -m spacy download en_core_web_sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e4e7fd00-42de-489e-bbcf-59df9ed69e38",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/app\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[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]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     /root/nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package maxent_ne_chunker to\n",
      "[nltk_data]     /root/nltk_data...\n",
      "[nltk_data]   Package maxent_ne_chunker is already up-to-date!\n",
      "[nltk_data] Downloading package words to /root/nltk_data...\n",
      "[nltk_data]   Package words is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import string\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
    "from nltk import pos_tag, ne_chunk\n",
    "from nltk.chunk import tree2conlltags\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('maxent_ne_chunker')\n",
    "nltk.download('words')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "pd.set_option('display.width', 1000)\n",
    "pd.options.display.max_colwidth=120\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f76c3e6-bf79-4fdc-b0b6-7b9f82ee57f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = [\"Id\",\"Entity\",\"Target\",\"Text\"]\n",
    "data = pd.read_csv(\"/app/data/twitter_training.csv\", names=columns,header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9b31f78-8230-4d42-a552-befb3a991864",
   "metadata": {},
   "outputs": [
    {
     "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>Id</th>\n",
       "      <th>Entity</th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will murder you all ,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>I am coming to the borders and I will kill you all,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands and i will kill you all,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im coming on borderlands and i will murder you all,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2401</td>\n",
       "      <td>Borderlands</td>\n",
       "      <td>Positive</td>\n",
       "      <td>im getting on borderlands 2 and i will murder you me all,</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id       Entity    Target                                                       Text\n",
       "0  2401  Borderlands  Positive      im getting on borderlands and i will murder you all ,\n",
       "1  2401  Borderlands  Positive        I am coming to the borders and I will kill you all,\n",
       "2  2401  Borderlands  Positive         im getting on borderlands and i will kill you all,\n",
       "3  2401  Borderlands  Positive        im coming on borderlands and i will murder you all,\n",
       "4  2401  Borderlands  Positive  im getting on borderlands 2 and i will murder you me all,"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41cfde05-d1d0-4eaf-9188-7e5fcf275515",
   "metadata": {},
   "outputs": [
    {
     "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>Text</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>im getting on borderlands and i will murder you all ,</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I am coming to the borders and I will kill you all,</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>im getting on borderlands and i will kill you all,</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>im coming on borderlands and i will murder you all,</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>im getting on borderlands 2 and i will murder you me all,</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        Text    Target\n",
       "0      im getting on borderlands and i will murder you all ,  Positive\n",
       "1        I am coming to the borders and I will kill you all,  Positive\n",
       "2         im getting on borderlands and i will kill you all,  Positive\n",
       "3        im coming on borderlands and i will murder you all,  Positive\n",
       "4  im getting on borderlands 2 and i will murder you me all,  Positive"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = data[[\"Text\",\"Target\"]]\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dcfb921c-8cea-4405-b30d-979250d40f6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 74682 entries, 0 to 74681\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Text    73996 non-null  object\n",
      " 1   Target  74682 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58c6acfb-c5a8-448f-a90e-8d31b9c232fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train= df_train.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf48fd1d-450d-474e-920b-aa411c10538b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 69773 entries, 0 to 74681\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Text    69769 non-null  object\n",
      " 1   Target  69773 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7900333a-e5a7-40a4-bd82-367331c49d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Target', ylabel='count'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Target\",data=df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1dbbb25a-9b4c-41cf-a1bd-b0e221bf2835",
   "metadata": {},
   "outputs": [
    {
     "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>Id</th>\n",
       "      <th>Entity</th>\n",
       "      <th>Target</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3364</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>Irrelevant</td>\n",
       "      <td>I mentioned on Facebook that I was struggling for motivation to go for a run the other day, which has been translate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>352</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>BBC News - Amazon boss Jeff Bezos rejects claims company acted like a 'drug dealer' bbc.co.uk/news/av/busine…</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8312</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>Negative</td>\n",
       "      <td>@Microsoft Why do I pay for WORD when it functions so poorly on my @SamsungUS Chromebook? 🙄</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4371</td>\n",
       "      <td>CS-GO</td>\n",
       "      <td>Negative</td>\n",
       "      <td>CSGO matchmaking is so full of closet hacking, it's a truly awful game.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4433</td>\n",
       "      <td>Google</td>\n",
       "      <td>Neutral</td>\n",
       "      <td>Now the President is slapping Americans in the face that he really did commit an unlawful act after his  acquittal! ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id     Entity      Target                                                                                                                     Text\n",
       "0  3364   Facebook  Irrelevant  I mentioned on Facebook that I was struggling for motivation to go for a run the other day, which has been translate...\n",
       "1   352     Amazon     Neutral            BBC News - Amazon boss Jeff Bezos rejects claims company acted like a 'drug dealer' bbc.co.uk/news/av/busine…\n",
       "2  8312  Microsoft    Negative                              @Microsoft Why do I pay for WORD when it functions so poorly on my @SamsungUS Chromebook? 🙄\n",
       "3  4371      CS-GO    Negative                                                  CSGO matchmaking is so full of closet hacking, it's a truly awful game.\n",
       "4  4433     Google     Neutral  Now the President is slapping Americans in the face that he really did commit an unlawful act after his  acquittal! ..."
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_val = pd.read_csv(\"/app/data/twitter_validation.csv\", names=columns,header=None)\n",
    "data_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6effae16-f7a4-451e-ad5e-008b1585c0aa",
   "metadata": {},
   "outputs": [
    {
     "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>Text</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I mentioned on Facebook that I was struggling for motivation to go for a run the other day, which has been translate...</td>\n",
       "      <td>Irrelevant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BBC News - Amazon boss Jeff Bezos rejects claims company acted like a 'drug dealer' bbc.co.uk/news/av/busine…</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@Microsoft Why do I pay for WORD when it functions so poorly on my @SamsungUS Chromebook? 🙄</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CSGO matchmaking is so full of closet hacking, it's a truly awful game.</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Now the President is slapping Americans in the face that he really did commit an unlawful act after his  acquittal! ...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                      Text      Target\n",
       "0  I mentioned on Facebook that I was struggling for motivation to go for a run the other day, which has been translate...  Irrelevant\n",
       "1            BBC News - Amazon boss Jeff Bezos rejects claims company acted like a 'drug dealer' bbc.co.uk/news/av/busine…     Neutral\n",
       "2                              @Microsoft Why do I pay for WORD when it functions so poorly on my @SamsungUS Chromebook? 🙄    Negative\n",
       "3                                                  CSGO matchmaking is so full of closet hacking, it's a truly awful game.    Negative\n",
       "4  Now the President is slapping Americans in the face that he really did commit an unlawful act after his  acquittal! ...     Neutral"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_val = data_val[['Text', 'Target']]\n",
    "df_val.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b70ae462-5f8a-4b53-bf0c-5d0a81909f99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   Text    1000 non-null   object\n",
      " 1   Target  1000 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_val.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e000dec3-f734-4085-8e16-2744c9dfd60e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Target', ylabel='count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Target\",data=df_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a77e880-d1d0-4c2f-ad9f-109d17160545",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
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 "nbformat_minor": 5
}