/Feedo-Up

Welcome to FEEDO_UP, a pioneering sentiment analysis website designed to unlock actionable insights from product reviews. As e-commerce continues to grow, understanding customer feedback becomes increasingly vital.

Primary LanguageJupyter Notebook

FEEDO _UP - Sentiment Analysis for Product Reviews

"Unveiling the Power of Sentiment Analysis in Product Review Exploration"

FEEDO_UP leverages advanced Natural Language Processing (NLP) techniques to provide a user-friendly platform for analyzing sentiments expressed in product reviews.

Table of Contents

  1. Introduction
  2. Features
  3. Technologies Used
  4. Future Scope
  5. How to Use
  6. Contributing
  7. License
  8. Contact

Introduction

FEEDO _UP is an innovative sentiment analysis tool designed to analyze and extract insights from product reviews across e-commerce platforms. Leveraging advanced web scraping, data preprocessing, and natural language processing (NLP) techniques, this tool helps users make informed decisions based on comprehensive sentiment analysis.

Features

  • Web Scraping: Automatically retrieve product reviews from Flipkart.
  • Sentiment Analysis: Analyze sentiments using NLP techniques to categorize reviews.
  • Customizable Analysis: Tailor the analysis based on specific criteria like product features or geographic preferences.
  • User-Friendly Interface: Intuitive design for easy input of product URLs and criteria selection.
  • Visualization: Clear and insightful visual representation of sentiment analysis results.

Technologies Used

-Programming Languages: Python. -Frameworks: Flask (for the web application), BeautifulSoup (for web scraping). -Natural Language Processing: NLTK (Natural Language Toolkit). -Data Visualization: Matplotlib, Seaborn. -Version Control: Git, GitHub.

Future Scope

FEEDO_UP is currently tailored for Flipkart, but future plans include:

  • Expanding support to other e-commerce platforms.
  • Implementing a robust feedback system.
  • Adding user login functionality.
  • Introducing a customer similarity feature to enhance personalized recommendations.

How to Use

  1. Clone the Repository: 
    git clone https://github.com/SUDIPA9002/feedo_up.git
cd feedo_up
  2. Install Dependencies: 
    pip install -r requirements.txt
  3. Run the Application: 
    python app.py
  4. Access the Web Interface: Open your browser and go to http://localhost:5000.

Contributing

We welcome contributions from the community! Feel free to open issues or submit pull requests to help improve FEEDO _UP.To contribute:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature/your-feature-name).
  3. Commit your changes (git commit -m 'Add some feature').
  4. Push to the branch (git push origin feature/your-feature-name).
  5. Create a new Pull Request. Please ensure your code follows the project's coding guidelines and includes appropriate tests.

License

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

Contact

For any questions or suggestions, please reach out to us: