🛍️ Customer Review Sentiment Analyzer

This is a collaborative team project. This repository is maintained as a separate portfolio copy with permission from the project team and is shared for learning, academic, and portfolio purposes.

📌 Project Overview

The Customer Review Sentiment Analyzer is a web application that uses Natural Language Processing (NLP) and Machine Learning to analyze customer reviews and predict their sentiment. The project combines a modern React-based frontend with a Flask backend that processes review text and performs sentiment classification using a trained machine learning model. The goal of the project is to demonstrate how machine learning and web development can be integrated to analyze customer feedback and provide meaningful sentiment insights.

✨ Features

• 🔍 Real-Time Sentiment Analysis using a trained ML model
• 🧠 Preprocessing with NLTK (stopwords removal, stemming, cleaning)
• 💬 Interactive Chatbot UI positioned like support bots on e-commerce sites
• 🎨 Responsive Frontend built with React + Tailwind CSS
• ⚡ Fast Performance powered by Vite
• 🚀 Modular & Scalable Codebase (ideal for extending to product reviews, feedback systems, etc.)

🤖 Machine Learning Workflow

The sentiment analysis pipeline involves:

Review text preprocessing Natural Language Processing Text vectorization using TF-IDF Machine Learning model training Sentiment classification Model integration with Flask Frontend-backend integration 👥 Project Information

Project Type: Collaborative Team Project

This project was developed collaboratively as part of a team. This repository is maintained as a separate portfolio copy with permission from the project team. Individual contributions and complete team member details can be documented here as the repository is further updated.

🔮 Future Enhancements

Improve sentiment classification accuracy Support Positive, Negative, and Neutral sentiment categories Add sentiment confidence scores Add review analytics and visualizations Add batch review analysis Improve frontend UI/UX Deploy the complete frontend and backend application

📷 Preview

Chatbot UI in the corner — lightweight and clean.

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🧰 Tech Stack

🔗 Frontend

• React
• Vite
• Tailwind CSS
• Axios – API handling

🧠 Backend

• Flask
• Pickle – to load the trained model
• NLTK – for text preprocessing
• scikit-learn – model training (offline)

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⚙️ Installation & Setup

1. Clone the Repository

https://github.com/prathmesh-sargar/Sentiment-Bot.git
cd sentiment-chatbot

Bacend setup :

pip install Flask Flask-Cors nltk scikit-learn

After installing, don’t forget to download NLTK stopwords:

python Shell run this

import nltk
nltk.download('stopwords')  

📌 This is required to perform text preprocessing like removing common stopwords (e.g., "is", "the", "and").