Sentiment Analysis on Product Reviews using LLMs

This project explores multi-label sentiment classification on product reviews using state-of-the-art large language models (LLMs) like LLAMA, GPT, and BERT. Built using Python and notebook-based workflows, the goal is to analyze real-world review text and classify nuanced sentiments using transformer-based models and NLP techniques.

Key Features

• Preprocessing of real-world review datasets for multi-label classification
• Fine-tuning and evaluation of LLMs including LLAMA, GPT, and BERT
• Implementation of tokenization, attention masking, and embeddings
• Model evaluation using accuracy, F1 score, and confusion matrices
• Notebook-based implementation for experimentation and visualization

Tools & Libraries

• Python (Jupyter Notebook)
• Hugging Face Transformers
• LLAMA, GPT, BERT models
• Scikit-learn
• Pandas, NumPy
• Matplotlib / Seaborn for plots

Use Case

Brands and retailers can use this system to:

• Detect nuanced sentiment beyond just positive/negative (e.g., angry, confused, impressed)
• Improve customer service response systems
• Feed clean data into product teams for iterative feedback

Future Enhancements

• Extend to multilingual review datasets
• Integrate with a Streamlit dashboard for live prediction demo
• Add explainability using SHAP or attention heatmaps

Author

Sai Ranjith Reddy
LinkedIn | GitHub

License

MIT – Free to use, share, and modify with attribution.