Bitcoin Sentiment and Trend Analysis

Overview

This project analyzes sentiment from Bitcoin-related tweets and compares the sentiment with current Bitcoin values to predict whether it is a good time to buy Bitcoin. The project combines sentiment analysis and trend prediction using advanced machine learning and natural language processing techniques.

Features

• Sentiment Analysis: Utilizes NLTK's sentiment analyzer to determine the overall sentiment (positive, negative, or neutral) of tweets related to Bitcoin.
• Trend Prediction: Uses an LSTM model to predict future trends in Bitcoin prices based on historical data.
• Decision Support: Combines sentiment analysis and trend predictions to generate actionable insights on Bitcoin investments.
• Report Generation: Generates a comprehensive report using Google Gemini Pro API.

Tools and Technologies

• Python
• NLTK: For sentiment analysis
• LSTM: For Bitcoin trend prediction
• Google Gemini Pro API: For generating detailed reports
• Libraries: NumPy, Pandas, Matplotlib, TensorFlow/Keras, Tweepy (for Twitter API)

Installation

1. Clone this repository:

   git clone https://github.com/Bhargavmupparisetty/Bitcoin-Sentiment-and-Trend-Analysis.git

2. Navigate to the project directory:

   cd Bitcoin-Sentiment-and-Trend-Analysis

Usage

1. Set Up Google Gemini pro API Keys: Add your Google Gemini Pro API key

Results

• Sentiment analysis provides real-time insights into public opinion about Bitcoin.
• LSTM model predicts future Bitcoin price trends with reasonable accuracy.
• The combined analysis offers actionable insights for investment decisions.

Future Enhancements

• Add support for additional cryptocurrencies.
• Incorporate more advanced sentiment analysis models like BERT.
• Include data from alternative social media platforms.
• Improve trend prediction accuracy with ensemble models.