Sentistat - Tweet Sentiment Analysis

Overview

This project analyzes public sentiment from Twitter data using Python and Natural Language Processing (NLP) techniques. Raw tweets are cleaned, processed, and classified into positive, negative, or neutral sentiments to understand overall opinion trends.

Features

• Preprocessing of tweet text including removal of URLs, mentions, hashtags, and stopwords
• Tokenization and sentiment polarity analysis using NLP libraries
• Classification of tweets into positive, negative, and neutral categories
• Visualization of sentiment distribution for easier interpretation

Technologies Used

• Python
• NLTK / TextBlob
• Pandas, NumPy
• Matplotlib / Seaborn

Workflow

1. Load Twitter dataset
2. Clean and preprocess tweet text
3. Perform sentiment analysis
4. Classify sentiment polarity
5. Visualize sentiment results

Use Cases

• Social media sentiment analysis
• Brand and product opinion tracking
• Public reaction analysis to events