📊 Sentiment Analysis System

📌 Overview

The Sentiment Analysis System is a full-stack application designed to analyze customer product reviews and classify them into positive, negative, or neutral sentiments.
The goal of this project is to help companies understand customer emotions at scale, identify patterns in feedback, and improve product quality based on real user opinions.

This project was built using a provided dataset of customer reviews and applies Natural Language Processing (NLP) and Machine Learning techniques to perform sentiment classification.

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🎯 Objectives

• Classify customer reviews into sentiment categories
• Analyze the distribution of customer emotions
• Help businesses make data-driven product improvements
• Demonstrate practical application of NLP and ML concepts

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✨ Features

• Preprocessing of raw customer review data
• Sentiment classification into Positive / Negative / Neutral
• Backend logic for data processing and prediction
• Frontend interface for displaying results
• Dataset-driven analysis for real-world relevance

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🧠 Tech Stack

Backend

• Python
• Machine Learning & NLP
  • Pandas
  • Scikit-learn
  • NLP preprocessing techniques

Frontend

• HTML
• CSS
• JavaScript

Data

• CSV dataset containing customer product reviews

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📂 Project Structure

SentimentAnalysis/ ├── back end/ # Backend ML & NLP logic ├── front-end/ # Frontend UI ├── redmi6.csv # Sample dataset (product reviews) ├── render.yaml # Deployment configuration ├── README.md # Project documentation

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⚙️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/VarshiniNeralla/SentimentAnalysis.git
cd SentimentAnalysis

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2️⃣ Backend Setup

cd back\ end

python -m venv venv

.\venv\Scripts\activate # Windows

pip install -r requirements.txt

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3️⃣ Frontend Setup

cd ../front-end

npm install

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4️⃣ Run the Application

• Start the backend server
• Run the frontend application
• Access the application through the configured localhost port

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🔄 Workflow

• Load customer review dataset (CSV)
• Clean and preprocess textual data
• Apply sentiment classification model
• Categorize reviews as positive, negative, or neutral
• Display sentiment insights through the frontend

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📈 Use Cases

• Product review analysis
• Customer feedback monitoring
• Market sentiment analysis
• Business intelligence support