BSc IT Project Guide: Product Review Sentiment Analysis
1. Project Title
Product Review Sentiment Analysis
2. Objective
The objective of this project is to build a web-based or software application that uses natural language processing (NLP) techniques to analyze product reviews from e-commerce platforms and determine the sentiment (positive, negative, or neutral) of each review. The insights can be used by businesses to improve their products and customer service.
3. Scope
This application allows users to input product reviews and returns sentiment results. It can fetch reviews from online sources or accept user-uploaded text. Visualization tools will be integrated to display the overall sentiment distribution.
4. Modules
- Review Input Module
- Sentiment Analysis Engine (using NLP models like VADER or TextBlob)
- Review Classification (positive/negative/neutral)
- Visualization Dashboard (charts of sentiment trends)
- Admin/User Authentication Module (optional)
5. Tools and Technologies
- Programming Languages: Python, JavaScript
- Libraries: NLTK, TextBlob, VADER, pandas, matplotlib
- Frameworks: Flask/Django (Backend), React/Vue (Frontend)
- Database: MySQL, SQLite or MongoDB
- Deployment: Heroku, AWS, or local server
6. System Requirements
Minimum:
- Processor: Intel i3
- RAM: 4GB
- Storage: 250GB
Recommended:
- Processor: Intel i5 or higher
- RAM: 8GB
- Storage: 500GB SSD
7. Future Enhancements
- Multi-language sentiment analysis
- Integration with social media reviews
- Advanced ML models like BERT for deeper sentiment understanding
8. Conclusion
This project offers a practical solution for analyzing large volumes of product reviews. It helps businesses gain insights into customer satisfaction and improve product offerings based on sentiment trends.