Sentiment Analysis on Electronic Product Reviews

Project Overview

This project performs sentiment analysis on electronic product reviews scraped from Flipkart. The goal is to classify reviews as positive or negative, and based on this, a recommendation system is developed that displays the percentage of positive and negative reviews for various products. This helps potential buyers make informed decisions when purchasing products.

Features

• Review Scraping: Automated scraping of product reviews from Flipkart.
• Data Preprocessing: Includes cleaning, tokenization, and vectorization of the review data.
• Sentiment Analysis: Uses a machine learning model to classify the sentiment of the reviews as positive or negative.
• Recommendation System: Displays the positive and negative review percentages for each product to help users make purchasing decisions.

Technology Stack

• Frontend:
  • HTML
  • CSS
  • Tailwind CSS
  • JavaScript
• Backend:
  • Python Django
  • SQLite3 (Database)
• Machine Learning:
  • Natural Language Processing (NLP) techniques for sentiment analysis
  • Libraries: NLTK, Scikit-learn

Project Architecture

1. Web Scraping: Collects product reviews data from Flipkart.
2. Preprocessing: Prepares the scraped data by cleaning, tokenizing, and converting it into a format suitable for the machine learning model.
3. Sentiment Analysis:
   • Applies a machine learning classifier to predict the sentiment of each review.
   • Classifies the reviews into positive or negative categories.
4. Recommendation System: Calculates the percentage of positive and negative reviews for each product and displays it to the users.
5. User Interface: Presents the reviews and sentiment analysis results in a clean, user-friendly interface.

How to Run the Project

1. Clone the repository:

   git clone https://github.com/Satish-Kumar-Verma/Sentiment-Analysis-on-Electronic-Product-Reviews.git
   cd Sentiment-Analysis-on-Electronic-Product-Reviews

2. Install dependencies:

   pip install -r requirements.txt

3. Apply migrations:

   python manage.py migrate

4. Run the Django development server:

   python manage.py runserver

5. Access the project via your browser at http://127.0.0.1:8000/.

Key Components

• Scraper Module: Collects data from Flipkart’s product pages.
• Sentiment Classifier: A machine learning model using Natural Language Processing (NLP) to classify sentiment.
• Web Interface: Built using Django and Tailwind CSS, provides an intuitive and responsive design.
• SQLite Database: Stores the scraped reviews and sentiment classification results.

Future Improvements

• Expand the dataset by scraping reviews from multiple e-commerce platforms.
• Enhance the machine learning model by incorporating advanced NLP techniques.
• Implement user login and session management for personalized recommendations.

License

This project is licensed under the MIT License. See the LICENSE file for details.