Visualization of Hidden Patterns in WhatsApp Chat Using Sentimental Analysis

Challenges include handling unstructured chat data, ensuring privacy and ethical standards, managing large data volumes, accurately interpreting sentiment despite context, sarcasm, and irony, and creating effective visualizations. The proposed paper aims to develop a robust chat analyzer for WhatsApp conversations, leveraging Python libraries like pandas, matplotlib, seaborn, and natural language processing techniques. By filtering out irrelevant messages and group notifications, the tool focuses on meaningful interactions and employs sentiment analysis to provide insights into user dynamics. This comprehensive analyzer offers visualization and statistical analysis of chat data through a user-friendly application deployed on the Heroku web platform. The project improves comprehension of WhatsApp messages by integrating machine learning and NLP, allowing users to extract important data and handle unwanted content effectively. The suggested procedure involves utilizing a machine learning algorithm that combines VADER sentiment analysis for accurate emotion detection with keyword frequency analysis for trend identification. The new system shows a 15% boost in accuracy and a 20% increase in processing efficiency for sentiment analysis and user activity detection when compared to current models.