Authors: B. Naresh, A.Vinaya Sri
Abstract: With the explosion of instant messaging apps comes massive volumes of unstructured conversational data, making it harder than ever to spot important patterns of communication and trends in behaviour. One of the most widely used messaging platforms, WhatsApp, stores data on user interactions, facial expressions, and conversation patterns. Still, there are a lot of challenges to solving when analysing this kind of data, like handling casual writing, eliminating unnecessary messages, comprehending sarcasm and contextual meanings, safeguarding user privacy, and efficiently processing massive datasets. In order to find hidden patterns in communication, this research presents a smart WhatsApp conversation analyser that employs sentiment analysis, natural language processing, and interactive data visualisation. To prepare exported WhatsApp chat files for sentiment analysis using the VADER (Valence Aware Dictionary and sEntiment Reasoner) algorithm, the proposed system removes medium messages, unnecessary notifications, and irrelevant information. The system examines sentiment, keyword frequency, user activity, message analytics, and visual exploration using Python modules like Pandas, Matplotlib, and Seaborn. Users are provided with an interactive web application that presents analytical data in order to assist them better understand communication behaviour and emotional tendencies inside talks. Experimental evaluation shows that the proposed framework improves sentiment classification accuracy by around 15% and boosts processing efficiency by more than 20% when compared to conventional conversation analysis methods. The developed approach provides a workable solution to the challenge of extracting actionable information from WhatsApp conversations using machine learning and natural language processing.
DOI: http://doi.org/10.5281/zenodo.21374832
