🛡 Sentiment Analysis System

The Sentiment Analysis System is a Python-based Natural Language Processing (NLP) application that analyzes user-entered text and determines the emotional tone of the sentence. The system classifies the input as Positive, Negative, or Neutral and also provides a confidence percentage based on polarity score.

In addition to basic sentiment detection, the application includes emotion identification such as Happy, Sad, Angry, or Excited using keyword-based logic. The program runs in a continuous loop, allowing users to analyze multiple sentences until they choose to exit.

The system is built using:

Python

TextBlob (for polarity analysis)

NLTK corpora (for language processing support)

This project demonstrates practical implementation of NLP concepts including text preprocessing, sentiment polarity scoring, conditional classification, and user interaction handling.

🎯 Key Features

Real-time user input sentiment analysis

Sentiment classification (Positive / Negative / Neutral)

Confidence percentage display

Basic emotion detection

Continuous loop execution

Simple command-line interface

🚀 Use Cases

Social media trend analysis

Customer feedback evaluation

Product review monitoring

Public opinion tracking