Machine Learning
Product Review Sentiment Analysis
Classify reviews as positive or negative.
Project overview
Product Review Sentiment Analysis is a mentor-guided ML project designed for university submission. Sentiment classification on product reviews with preprocessing, TF-IDF or embeddings, and visualization of class distribution. The build uses Python, scikit-learn, NLTK and is structured so you can explain architecture, modules, and test cases during viva. Typical delivery timeline is 4–6 weeks depending on package and college deadlines.
Best suited for: Students working on predictive analytics and applied ML projects
Tech stack
Objectives
- Design and implement a working solution for “Product Review Sentiment Analysis” aligned to academic evaluation criteria.
- Demonstrate core features such as Text clean, Model compare, Confusion matrix.
- Produce documentation (synopsis, report chapters, and PPT) suitable for guide approval.
- Prepare viva-ready explanations for design choices, tech stack, and future enhancements.
Key modules
- Module: Text clean
- Module: Model compare
- Module: Confusion matrix
- Documentation, deployment notes, and viva Q&A
Key features
- Text clean
- Model compare
- Confusion matrix
- Demo predict
Deliverables
- Notebook
- Report
- PPT
Learning outcomes
- Runnable source code with setup guide
- University-style report draft
- Presentation deck (PPT)
- Mentor review checkpoints
