💬 Sentiment Analysis App

🎓 CodeC Technologies Internship – Task 2

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📌 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.

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🚀 Features

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

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🖼️ Screenshots

📌 Input Interface

📊 Prediction Output

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🧠 Technologies Used

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

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🧪 Model Details

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

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🔄 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

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📂 Project Structure