ChatProbe Profiling WhatsApp Conversations Using Machine Learning Approaches

Abstract: WhatsApp has become integral to modern communication, yet managing unwanted messages and group notifications presents challenges for effective analysis. The model present in paper will address this issue by developing a robust chat analyzer capable of handling such content. Utilizing Python libraries like pandas, seaborn, and matplotlib, alongside advanced natural language processing, the analyzer identifies and filters out irrelevant messages and group notifications. A preprocessing module ensures that subsequent analysis focuses on meaningful conversations, while sentiment analysis provides insights into user interactions. Deployed as a user-friendly application, the analyzer offers comprehensive visualization and statistical analysis of chat data. Through interactive features, users gain valuable insights into conversation dynamics while efficiently managing unwanted content. Incorporating machine learning and sentiment analysis, this project presents a versatile solution for WhatsApp conversation analysis, empowering users to extract meaningful information while mitigating the impact of unwanted content. It is deployed on the Heroku web platform, utilizes a combination of Python libraries such as matplotlib, streamlit, seaborn, re, pandas, and concepts of natural language processing. This amalgamation of machine learning and NLP techniques enables the tool to import WhatsApp chat files, analyze them, and generate various visualizations, enhancing comprehension of the data.

Keywords: WhatsApp, Chat Analysis, Emoji Analysis, Emotion Analysis, Sentiment Analysis, Preprocessing, Natural Language Processing, Data Visualization, Machine Learning, Python Libraries, Pandas, Seaborn, Matplotlib, Text Analysis, Behavioural Analysis, Group Notifications, Unwanted Messages, User Interaction. Cite: Sravanthi D, Lepakshi Reddy S, Vittal Sai C, Jahnavi S, Vamsi C, "ChatProbe Profiling WhatsApp Conversations Using Machine Learning Approaches", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13423.