A natural language processing project that classifies tweet sentiment using the Naive Bayes algorithm.
This project analyzes Twitter data to classify sentiment (positive, negative, neutral) using machine learning. The implementation uses the Naive Bayes classifier and evaluates model performance through accuracy metrics.
├── Group Project (1)m.ipynb # Main notebook with code and analysis
├── _Assignment3_Team1_Report.pdf # Detailed report and analysis
└── README.md
- Algorithm: Naive Bayes classifier
- Task: Sentiment classification from tweet text
- Evaluation: Accuracy-based performance metrics
pip install pandas numpy scikit-learn nltk- Open the Jupyter notebook
Group Project (1)m.ipynb - Ensure your dataset is loaded (or update the data path)
- Run all cells to preprocess, train, and evaluate the model
The notebook includes:
- Data preprocessing and text cleaning
- Naive Bayes model training
- Accuracy evaluation and metrics
- Sentiment classification examples
For detailed methodology, analysis, and findings, refer to the PDF report.
Assignment 3 – Team 1
This project is available for educational purposes.
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