/tw1tter_bot_b1nance

Primary LanguagePython

Twitter Crypto Sentiment Analyzer

This Django-based application connects to Twitter, monitors tweets from specific users in real-time, and performs sentiment analysis on those tweets concerning cryptocurrency tokens. The analyzed results are then broadcasted via a WebSocket to be displayed in a web interface.

Table of Contents

  1. [Features]
  2. [Directory Structure]
  3. [Installation]
  4. [Usage]
  5. [Contributing]
  6. [License]

1 Features

  • Real-time tweet monitoring from pre-defined Twitter users.
  • Sentiment analysis using OpenAI to determine the sentiment towards cryptocurrency tokens mentioned in the tweet.
  • WebSocket integration for live updates in the web interface.
  • Efficient database structures for storing tweets, users, and sentiment analysis results.

2 Directory Structure

.
├── analyze_tweet           # Contains code for sentiment analysis.
├── callTwitter             # Handles real-time monitoring of Twitter.
├── database_manager        # ORM models and database utilities.
├── static/twitter          # Static assets for the web interface.
├── tw1tter_bot_b1nance     # Main application module.
├── twitter                 # Additional module.
├── websocketProcessor      # Handles WebSocket connections and broadcasting.
├── .env.SAMPLE             # Sample environment variables.
├── .gitignore              # Git ignore file.
├── Dockerfile              # Docker configuration file.
├── db.sqlite3              # SQLite database file.
├── docker-compose.yml      # Docker compose configuration.
└── manage.py               # Django management script.

Installation

Prerequisites

  • Python 3.8+
  • Docker (optional)

Steps

  1. Clone the Repository:

    git clone https://github.com/yourusername/twitter-crypto-sentiment-analyzer.git
cd twitter-crypto-sentiment-analyzer

  2. Set up a Virtual Environment:

    python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  3. Install Dependencies:

    pip install -r requirements.txt

  4. Set up Environment Variables:

    • Rename .env.SAMPLE to .env.
    • Fill in the required environment variables like OPENAI_API_KEY, TWITTER_KEY, etc.

  5. Run Migrations:

    python manage.py migrate

  6. (Optional) Using Docker:

    docker-compose up --build

Usage

  1. Start the Django Server:

    python manage.py runserver

  2. Access the Web Interface:

    • Open a browser and navigate to http://localhost:8000/.

  3. Monitoring Tweets:

    • The application will automatically start monitoring tweets from the specified users in calltwitter.py.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See the LICENSE file for details.