Published Paper Details:

Social media platforms generate massive volumes of data every second, making it difficult for individuals and organizations to manually analyze public opinion and engagement trends. Twitter (X), being one of the most influential microblogging platforms, plays a major role in shaping public sentiment, brand perception, and digital communication. This project presents an AI-Driven Twitter Analytics Dashboard built using Python, Streamlit, Plotly, and Natural Language Processing (NLP) techniques. The system analyzes Twitter posts and provides real-time insights into user engagement and public sentiment. It processes tweet data and extracts key performance metrics such as likes, retweets, replies, views, and engagement rate. Using TextBlob sentiment analysis, the application automatically classifies tweets into positive, neutral, and negative categories. The dashboard visualizes results through interactive graphs including sentiment distribution, engagement histograms, and performance comparison charts. The platform also supports data export in CSV and Excel formats, enabling further offline analysis. This project demonstrates how artificial intelligence and data visualization can be combined to create an intelligent, user-friendly analytics tool for social media monitoring, brand tracking, and trend analysis