Abstract: The emergence of instant messaging apps such as WhatsApp has transformed communication and resulted in an enormous amount of conversational data being collected. By analyzing this data, important insights about user behavior, preferences, and new trends can be found. In this research, we suggest a novel method for applying machine learning (ML) techniques to the analysis of WhatsApp chat trends. Utilizing natural language processing (NLP) techniques, our suggested method preprocesses WhatsApp chat data and extracts pertinent information. We use named entity recognition to find important entities referenced in chats, topic modeling to find recurrent themes, and sentiment analysis to determine the emotional tone of discussions. We also use machine learning classifiers to group conversations according to other parameters like subject, sentiment, and participant demographics. We undertake trials on a huge dataset of WhatsApp chats covering a variety of themes and user demographics in order to verify the efficacy of our technique. We assess the precision of our topic modeling, sentiment analysis, and classification algorithms, showcasing their capacity to extract significant insights from chat data. Our findings demonstrate how our ML-based WhatsApp chat trends analyzer can be used to extract insightful information from conversational data. This study adds to the expanding body of knowledge in conversational analytics and has applications in sociolinguistics, social media marketing, and customer service optimization, among other areas. Our effort intends to bridge the gap between raw conversational data and actionable insights by offering a comprehensive framework for WhatsApp chat research. This will improve our understanding of digital communication patterns and their wider social consequences.
Keywords: Data Analysis, Named Entity Recognition, Machine Learning, Classification Algorithms, and Natural Language Processing.
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DOI:
10.17148/IJARCCE.2024.13579
