TweetFeel

TweetFeel is a Flask web app that performs real-time sentiment analysis on tweets scraped via twscrape. It features user authentication, an SQL database for storing tweets, and data visualizations through PyChart, including histograms and line graphs.

TweetFeel

TweetFeel is a Flask web application that performs real-time sentiment analysis on tweets scraped using twscrape. It features user authentication, an SQL database for storing tweets and user data, and interactive data visualizations with PyChart, including histograms and line graphs.

📈 Features

• Real-Time Tweet Scraping: Collects tweets based on keywords or hashtags using twscrape.
• Sentiment Analysis: Classifies tweets as positive, negative, or neutral.
• User Authentication: Secure login and registration system.
• SQL Database: Stores tweets and user information efficiently.
• Data Visualizations: Interactive graphs (histograms, line graphs) to display sentiment trends.

🛠️ Technologies Used

• Backend: Python, Flask, twscrape, SQL (SQLite/PostgreSQL)
• Frontend: HTML5, CSS3, JavaScript, PyChart

🚀 Installation

1. Clone the Repository

   git clone https://github.com/yourusername/TweetFeel.git
   cd TweetFeel

2. Create a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows: `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. Configure the Database

   • Update the database URI in config.py if necessary.
   • Initialize the database:

     flask db init
     flask db migrate -m "Initial migration."
     flask db upgrade

5. Run the Application

   flask run

6. Access the App

   • Open your browser and navigate to http://localhost:5000

📊 Usage

1. Register an Account: Create a new user to access TweetFeel.
2. Scrape Tweets: Enter keywords or hashtags to start scraping tweets in real-time.
3. View Sentiments: See sentiment analysis results categorized as positive, negative, or neutral.
4. Explore Visualizations: Use interactive graphs to analyze sentiment trends and patterns.

🖼️ Screenshots

🤝 Contributing

Contributions are welcome! Follow these steps:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/YourFeature)
3. Commit your changes (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License.

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