twitter-sentiment-analysis-NLP

Description

This project analyzes the sentiment of tweets using natural language processing (NLP). It uses a dataset containing 1.6 million tweets, labeled as positive or negative, to train a machine learning model. The goal is to classify the sentiment of tweets accurately, which can be useful for businesses, researchers, and developers looking to analyze public opinion.

#Dataset The dataset contains 1.6 million labeled tweets, with the following columns:

• target: Sentiment label (0 = negative, 4 = positive).
• text: The tweet content.

Source: Kaggle Twitter Sentiment Dataset.

Features

• Preprocess raw Twitter data (removing URLs, mentions, hashtags, etc.).
• Train a machine learning model (Logistic Regression, SVM, or others).
• Evaluate model accuracy using precision, recall, and F1-score.
• Classify new tweets in real-time.

Contribution

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.