Twitter Sentiment Analysis

Overview

The Twitter Sentiment Analysis project is designed to analyze public sentiment from tweets. By leveraging natural language processing (NLP) techniques, this project identifies and classifies tweets into different sentiment categories such as positive, negative, or neutral. This analysis can be applied to understand trends, public opinion, and user feedback in real time.

Features

• Dataset-Based Analysis: Uses an available dataset containing pre-collected tweets.
• Preprocessing and Analysis: Clean and preprocess tweets, then classify them into sentiment categories using machine learning models or pre-trained NLP libraries.
• Visualization: Generate insightful charts and graphs to represent sentiment distribution.

Requirements

• Python 3.7+
• Libraries:
  • numpy
  • pandas
  • matplotlib
  • seaborn
  • scikit-learn
  • nltk
  • WordCloud

Installation

1. Clone the repository:

   git clone https://github.com/AbhinavKumar777/twitter-sentiment-analysis.git
   cd twitter-sentiment-analysis

2. Install the required libraries:

   pip install -r requirements.txt

3. Place the dataset file in the project directory:

   • Ensure the dataset file is named train.csv or update the script configurations accordingly.

Usage

Run the script to preprocess the dataset, perform sentiment analysis, and visualize results, all in one go:

python twitter_sentiment_analysis.py

Project Structure

.
├── data
│   ├── test.csv
    ├── train.csv
├── twitter_sentiment_analysis.py
├── requirements.txt
├── README.md

Future Enhancements

• Integrate deep learning models like BERT or GPT for better accuracy.
• Extend to multilingual sentiment analysis.
• Build a dashboard for real-time sentiment visualization.
• Analyze temporal trends in sentiment.

Contributions

Contributions are welcome! Feel free to fork the repository and submit a pull request.

License

This project is licensed under the MIT License.

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Happy Analyzing!