Sentiment Analysis Web Application (NLP)

This project implements an end-to-end Sentiment Analysis system that classifies user-provided text as Positive or Negative using Natural Language Processing (NLP) and Machine Learning.
An interactive and user-friendly web interface is built using Gradio to demonstrate real-time predictions.

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Project Overview

Understanding customer sentiment is critical for businesses to improve products and services.
This project analyzes short text comments (such as food delivery feedback) and predicts the sentiment using a trained machine learning model.

The application allows users to input any text comment and instantly receive a sentiment prediction through a web-based interface.

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Key Features

• Text preprocessing and normalization
• Feature extraction using NLP techniques
• Machine learning–based sentiment classification
• Interactive web interface using Gradio
• Lightweight sample dataset for demonstration
• Clear visualization of prediction results

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Tech Stack

• Python
• Scikit-learn
• NLP (NLTK, TextBlob)
• Gradio (Web UI)
• Pandas & NumPy
• Matplotlib & Seaborn
• WordCloud
• Imbalanced-learn

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How to Run the Project

Step 1: Clone the repository

git clone https://github.com/Jayanth717/Sentiment-Analysis.git

Step 2: Navigate to the project directory

cd Sentiment-Analysis

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the application

python Final_Project.py

Author

Jayanth Kumar