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💬 Sentiment Analysis App

🎓 CodeC Technologies Internship – Task 2

📌 Project Description

This project is a Sentiment Analysis Web Application developed as part of Task 2 of the CodeC Technologies Internship.

It uses Natural Language Processing (NLP) and Machine Learning to classify text as:

  • 😊 Positive
  • 😡 Negative

The application allows users to input text and get real-time sentiment predictions along with confidence scores.

🚀 Features

  • ✍️ Enter text input
  • 🎯 Real-time sentiment prediction
  • 📊 Confidence score display
  • ⚡ Fast and lightweight model
  • 🎨 Clean and simple UI using Streamlit

🖼️ Screenshots

📌 Input Interface

Input

📊 Prediction Output

Output

🧠 Technologies Used

  • Python
  • Scikit-learn
  • Streamlit
  • Pandas
  • TF-IDF Vectorizer

🧪 Model Details

  • Model: Multinomial Naive Bayes
  • Dataset: Sentiment140 (Twitter dataset)
  • Input: Text data
  • Output: Positive / Negative
  • Accuracy: Good for basic sentiment classification

🔄 Working Process

  1. User enters text
  2. Text is converted into numerical features using TF-IDF
  3. Model predicts sentiment
  4. Confidence score is displayed
  5. Result is shown in UI

📂 Project Structure