Twitter Sentiment Analysis Tool

This project is a simple and effective Sentiment Analysis Tool built with Python, NLTK, and Scikit-learn. It analyzes the sentiment of social media text data (like tweets) and classifies them as positive, negative, or neutral.

🔍 Overview

The tool processes raw text data by performing:

• Tokenization
• Stop-word removal
• Lemmatization

Then it vectorizes the text using TF-IDF and uses a Naive Bayes classifier to predict the sentiment.

📁 Dataset

The model is trained on a Dataset, which contains 1.6 million tweets annotated for sentiment.

• File used: training.1600000.processed.noemoticon.csv
• Only text and target columns are used.
• Sampled 50,000 tweets for faster training and testing.

🛠️ Features

• Preprocesses social media text (removes URLs, mentions, hashtags)
• Supports live user input for prediction
• Real-time sentiment prediction
• Simple command-line interface

📦 Dependencies

Install the required Python packages using pip:

pip install pandas numpy nltk scikit-learn