Extraction of Valuable Insights of WhatsApp Chat Using VADER

Sentiment analysis is one of the captivating fields, it focuses on understanding emotions and attitudes expressed in text or symbolic language. In this fast-developing digital environment, communication is vital and needs to be swift and efficient. Communication via instant messaging has gained wide popularity. WhatsApp is one such tool which is a commonly used platform to communicate effectively with individuals or to a group in realtime, share documents, images, audio and videos, and also other file formats. The research analyzes WhatsApp chats, and offers insights into group conversations on diverse topics. A unique feature of this research is the ability to analyze group chats while focusing on providing individual insights of each member, fostering collaboration and information sharing. Additionally, it accommodates both 24-hour and 12-hour clock formats for chat compatibility. By exporting chat files and employing natural language processing (NLP) and machine learning techniques, the research provides various insightful plots and graphs, depicting message counts, emojis, images, and more. Leveraging Python modules such as NumPy, pandas, matplotlib, and Vader lexicon, the research paper achieves efficient and accurate analysis. It empowers businesses and individuals with a comprehensive understanding of their conversations, facilitating informed decision-making.