Twitter Data Mining

Overview

This project focuses on mining and analyzing data from Twitter. It involves collecting tweets, processing the data, and performing various analyses to extract meaningful insights.

Features

• Fetch tweets using the Twitter API
• Perform sentiment analysis on tweets
• Visualize tweet data with graphs and charts
• Store and retrieve tweet data from a database

Requirements

• Python 3.x
• Tweepy
• Pandas
• NLTK (Natural Language Toolkit)
• Matplotlib
• SQLite (or any other preferred database)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/twitter-data-mining.git
   cd twitter-data-mining

2. Install the required packages:

   pip install -r requirements.txt

3. Set up your Twitter API credentials:

   • Create a Twitter Developer account and generate API keys and tokens.
   • Create a file named config.py and add your credentials:

     CONSUMER_KEY = 'your-consumer-key'
     CONSUMER_SECRET = 'your-consumer-secret'
     ACCESS_TOKEN = 'your-access-token'
     ACCESS_TOKEN_SECRET = 'your-access-token-secret'

Usage

1. Fetch tweets:

   python fetch_tweets.py --query "your search query" --count 100

2. Perform sentiment analysis:

   python sentiment_analysis.py

3. Visualize data:

   python visualize_data.py

Project Structure