Add data preprocessing notebook and custom text preprocessing function

data_processing.ipynb

@@ -0,0 +1,964 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f738c6f3-948e-4ba0-a69b-97fd0ce22e84",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import sys\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": [
+    {
+     "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",
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+       "\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>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Communal violence in Bhainsa, Telangana. \"Ston...</td>\n",
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+       "      <td>Telangana: Section 144 has been imposed in Bha...</td>\n",
+       "      <td>0</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>\"Lord Jesus, your love brings freedom and pard...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                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": [
+    {
+     "data": {
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
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+      "text/plain": [
+       "                                                text  label\n",
+       "0  WASHINGTON (Reuters) - The head of a conservat...      0\n",
+       "1  WASHINGTON (Reuters) - Transgender people will...      0\n",
+       "2  WASHINGTON (Reuters) - The special counsel inv...      0\n",
+       "3  WASHINGTON (Reuters) - Trump campaign adviser ...      0\n",
+       "4  SEATTLE/WASHINGTON (Reuters) - President Donal...      0"
+      ]
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+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "real_news.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "3ed9a420-5bc9-432e-b8b6-5401cb3c83ab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
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+       "  <thead>\n",
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+       "      <th>0</th>\n",
+       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label\n",
+       "0  Donald Trump just couldn t wish all Americans ...      1\n",
+       "1  House Intelligence Committee Chairman Devin Nu...      1\n",
+       "2  On Friday, it was revealed that former Milwauk...      1\n",
+       "3  On Christmas day, Donald Trump announced that ...      1\n",
+       "4  Pope Francis used his annual Christmas Day mes...      1"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fake_news.head()"
+   ]
+  },
+  {
+   "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": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <th>label</th>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Telangana: Section 144 has been imposed in Bha...</td>\n",
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+       "      <td>Arsonist sets cars ablaze at dealership https:...</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>Arsonist sets cars ablaze at dealership https:...</td>\n",
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+       "      <th>4</th>\n",
+       "      <td>\"Lord Jesus, your love brings freedom and pard...</td>\n",
+       "      <td>1</td>\n",
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+       "  </tbody>\n",
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+      "text/plain": [
+       "                                                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": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "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",
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+    "tags": []
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+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>text</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>communal violence bhainsa telangana stones pel...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>telangana section 144 imposed bhainsa january ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>arsonist sets cars ablaze dealership</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>arsonist sets cars ablaze dealership</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>lord jesus love brings freedom pardon fill hol...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label\n",
+       "0  communal violence bhainsa telangana stones pel...      0\n",
+       "1  telangana section 144 imposed bhainsa january ...      0\n",
+       "2               arsonist sets cars ablaze dealership      0\n",
+       "3               arsonist sets cars ablaze dealership      0\n",
+       "4  lord jesus love brings freedom pardon fill hol...      1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "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": [
+    {
+     "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>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>name michael brown robbed local convenience st...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>washington reuters japanese prime minister shi...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>chicago reuters us house republican tax bill r...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>reuters fbi interviewed michael flynn initial ...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>harare reuters ousted zimbabwe finance ministe...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label\n",
+       "0  name michael brown robbed local convenience st...      1\n",
+       "1  washington reuters japanese prime minister shi...      0\n",
+       "2  chicago reuters us house republican tax bill r...      0\n",
+       "3  reuters fbi interviewed michael flynn initial ...      0\n",
+       "4  harare reuters ousted zimbabwe finance ministe...      0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "8b4318d6-b209-4587-b1f8-d469e29f83c6",
+   "metadata": {
+    "tags": []
+   },
+   "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>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>kraig moss die hard donald trump supporter fol...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>white house lawyers last month learned former ...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>awesome many levels hard know beginafghanistan...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>please note overwhelming information regarding...</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>kabul reuters us ambassador afghanistan said m...</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                text  label\n",
+       "0  kraig moss die hard donald trump supporter fol...      1\n",
+       "1  white house lawyers last month learned former ...      1\n",
+       "2  awesome many levels hard know beginafghanistan...      1\n",
+       "3  please note overwhelming information regarding...      1\n",
+       "4  kabul reuters us ambassador afghanistan said m...      0"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9ca92861-7526-4bb0-ad1b-50b3104acc02",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "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",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

preprocessing.py

@@ -0,0 +1,34 @@
+import re
+from nltk.corpus import stopwords
+
+def preprocess_text(df):
+    """
+    Preprocesses the text column in a DataFrame by applying various cleaning operations.
+
+    Args:
+        df (pandas.DataFrame): The DataFrame containing the text column to be preprocessed.
+
+    Returns:
+        None. The text column in the provided DataFrame is modified in place.
+    """
+    # Remove URLs, user mentions, non-alphanumeric characters and hashtags from the tweets
+    df['text'] = df['text'].apply(lambda x: re.sub(r'http\S+', '', str(x))) # remove URLs
+    df['text'] = df['text'].apply(lambda x: re.sub(r'@\S+', '', str(x))) # remove user mentions
+    df['text'] = df['text'].apply(lambda x: re.sub(r'[^a-zA-Z0-9\s]', '', str(x))) # remove non-alphanumeric characters
+    df['text'] = df['text'].apply(lambda x: re.sub(r'#\S+', '', str(x))) # remove hashtags
+    
+    # Remove punctuation and convert text to lowercase
+    df['text'] = df['text'].apply(lambda x: re.sub('[^\w\s]', '', x))
+    df['text'] = df['text'].apply(lambda x: x.lower())
+    
+    # Remove stop word (such as "a", "an", "the", "is", "of", etc.)
+    stop_words = set(stopwords.words('english'))
+    df['text'] = df['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words]))
+    
+    # Remove any remaining white space
+    df['text'] = df['text'].apply(lambda x: x.strip())
+    
+    # Remove observations with less than 3 words
+    df = df[df['text'].apply(lambda x: len(x.split()) >= 3)]
+    
+    return df