{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f738c6f3-948e-4ba0-a69b-97fd0ce22e84",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from kaggle.api.kaggle_api_extended import KaggleApi\n",
    "from sklearn.model_selection import train_test_split\n",
    "from glob import glob\n",
    "\n",
    "from preprocessing import preprocess_text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29a79e0d-9b44-40b0-81ab-7cc7eca2e77b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Introduction:\n",
    "This notebook aims to preprocess two datasets, the Disaster Tweet Dataset and the Fake/Real News Dataset, obtained from Kaggle using the Kaggle API. The goal is to bring both datasets into a consistent format with two columns: 'text' and 'label' **(0 for real, 1 for fake)**. The data will be split into train and test sets (80/20 ratio) and saved as CSV files.\n",
    "\n",
    "Process Overview:\n",
    "- **Dataset Acquisition:** Download the Disaster Tweet Dataset and Fake/Real News Dataset from Kaggle using the Kaggle API.\n",
    "- **Dataset Preprocessing:** Ensure a consistent format by keeping only the 'text' column, assigning labels (0 for real, 1 for fake), and merging the datasets. The text column in both datasets will undergo preprocessing using a custom function called `preprocess_text`. This function applies various cleaning operations to the text, including URL and user mention removal, non-alphanumeric character removal, hashtag removal, punctuation removal, lowercase conversion, stop word removal and keeping only texts containing at least 3 words.\n",
    "- **Train/Test Split:** Split the combined preprocessed dataset into train and test sets using an 80/20 ratio.\n",
    "- **Save Preprocessed Data:** Save the preprocessed train and test datasets as separate CSV files."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f758cf39-ccf4-477d-808b-ced13d80645d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Disaster tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ff78420b-e905-40ca-8453-432a7c193c18",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Instantiate the Kaggle API object\n",
    "api = KaggleApi()\n",
    "\n",
    "# Set the Kaggle API credentials\n",
    "api.authenticate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdeec404-b214-4d2b-8f17-189cf149062b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the dataset to download\n",
    "disaster_dataset_slug = 'vstepanenko/disaster-tweets'\n",
    "disaster_output_path = 'Data/disaster-tweets'\n",
    "\n",
    "# Download the dataset files\n",
    "api.dataset_download_files(disaster_dataset_slug, path=disaster_output_path, unzip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e3e01eb8-1182-4fd3-8252-2698ebc63e9f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "disaster_path = list(glob(disaster_output_path + '*/*'))[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "07641f87-7898-482c-9d14-5466ae2b396c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Data/disaster-tweets/tweets.csv'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disaster_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51f8a0a0-e85c-4bff-9881-c75dd6e14c9a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "disaster = pd.read_csv(disaster_path)\n",
    "disaster = disaster.drop(['id', 'keyword', 'location'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec69fa87-4ac7-469b-9de7-99d034127b31",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Invert the values in the 'Target' column\n",
    "disaster['target'] = disaster['target'].map({1: 0, 0: 1})\n",
    "disaster = disaster.rename(columns={'target': 'label'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dc0ddbb-be2b-4faa-a639-396bdda82f67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "                                                text  label\n",
       "0  Communal violence in Bhainsa, Telangana. \"Ston...      0\n",
       "1  Telangana: Section 144 has been imposed in Bha...      0\n",
       "2  Arsonist sets cars ablaze at dealership https:...      0\n",
       "3  Arsonist sets cars ablaze at dealership https:...      0\n",
       "4  \"Lord Jesus, your love brings freedom and pard...      1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disaster.head() # real: 0 | fake: 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83e8b2ca-783c-40ef-8240-868165c1a4b1",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Real/Fake News"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06fdce6a-4a29-47f8-813d-2fe74f883db9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the dataset to download\n",
    "news_dataset_slug = 'clmentbisaillon/fake-and-real-news-dataset'\n",
    "news_output_path = 'Data/fake-and-real-news-dataset'\n",
    "\n",
    "# Download the dataset files\n",
    "api.dataset_download_files(news_dataset_slug, path=news_output_path, unzip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2cf8b40-0c0b-4fdf-9fc0-5a3ed159a043",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "news_path = list(glob(news_output_path + '*/*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cce93c7f-c4fe-4123-bef5-742c9ab7c5dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Data/fake-and-real-news-dataset/Fake.csv',\n",
       " 'Data/fake-and-real-news-dataset/True.csv']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "news_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0f8ed26f-4c9a-4881-a23d-4d0024496999",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_path = news_path[1]\n",
    "fake_path = news_path[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2f990dc8-c5da-453b-82d1-36fa6d6c9d47",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_news = pd.read_csv(real_path)\n",
    "fake_news = pd.read_csv(fake_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd84c34e-a991-45ae-865d-74b7e870e203",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_news = real_news.drop(['title', 'subject', 'date'], axis=1)\n",
    "fake_news = fake_news.drop(['title', 'subject', 'date'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef055dba-c390-41ea-861d-c1cf9fc57211",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_news['label'] = 0\n",
    "fake_news['label'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "909dcc92-1d5e-4794-ae70-de8803117cfb",
   "metadata": {
    "tags": []
   },
   "outputs": [
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       "0  Donald Trump just couldn t wish all Americans ...      1\n",
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  },
  {
   "cell_type": "markdown",
   "id": "27f26a75-1f18-4762-971f-d17a20a9b587",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Concatenate the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c9ffb4f8-04a4-404a-8412-54e5f1a237f8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.concat([disaster, real_news, fake_news]).reset_index().drop(columns = 'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1a86a46d-25d0-4a25-914d-722754b91aa6",
   "metadata": {
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   "outputs": [
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       "                                                text  label\n",
       "0  Communal violence in Bhainsa, Telangana. \"Ston...      0\n",
       "1  Telangana: Section 144 has been imposed in Bha...      0\n",
       "2  Arsonist sets cars ablaze at dealership https:...      0\n",
       "3  Arsonist sets cars ablaze at dealership https:...      0\n",
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     "execution_count": 19,
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  {
   "cell_type": "markdown",
   "id": "c9b88873-63fe-43e6-91fd-7ad49c52b07a",
   "metadata": {},
   "source": [
    "# Preprocess the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b74564a5-1c89-4d85-b096-4a57c195234c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = preprocess_text(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c5ec549-1c07-4c2b-9357-69ea89b280cd",
   "metadata": {
    "tags": []
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   "outputs": [
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       "                                                text  label\n",
       "0  communal violence bhainsa telangana stones pel...      0\n",
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   "execution_count": 22,
   "id": "b2328328-e513-4bb8-9172-0d109d20797b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of REAL data: 42.61%\n",
      "Percentage of FAKE data: 57.39%\n"
     ]
    }
   ],
   "source": [
    "print(f'Percentage of REAL data: {round((len(data) - data[\"label\"].sum()) / len(data) * 100, 2)}%')\n",
    "print(f'Percentage of FAKE data: {round(data[\"label\"].sum() / len(data) * 100, 2)}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41564eef-4b4b-48a1-9cdb-cf615c753c83",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Train/Test split and save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "108aeb30-f294-4650-8ac7-afe96b97788b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Shuffle the DataFrame\n",
    "shuffled_data = data.sample(frac=1, random_state=42)  # Set random_state for reproducibility\n",
    "\n",
    "# Split the shuffled DataFrame into train and test sets\n",
    "train_data, test_data = train_test_split(shuffled_data, test_size=0.2, random_state=42)  # Adjust test_size as needed\n",
    "\n",
    "# Reset the index column\n",
    "train_data = train_data.reset_index().drop(['index'], axis=1)\n",
    "test_data = test_data.reset_index().drop(['index'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e95eb9a-8e96-4871-8caf-50ee8c8b2ab8",
   "metadata": {
    "tags": []
   },
   "outputs": [
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   "cell_type": "code",
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   "id": "8b4318d6-b209-4587-b1f8-d469e29f83c6",
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  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9ca92861-7526-4bb0-ad1b-50b3104acc02",
   "metadata": {
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   "source": [
    "# Save the train and test sets as separate CSV files\n",
    "train_data.to_csv('Data/train_dataset.csv', index=False)\n",
    "test_data.to_csv('Data/test_dataset.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "439efe82-51e4-49f4-908f-6f1c9bc3d3d1",
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   "source": []
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