WhatsApp Group Chat Personality Analysis Using AI and Machine Learning Techniques

Natural Language Processing (NLP) has significantly transformed the dynamics of human-computer interaction. NLP applications, ranging from machine translation and information extraction to summarization and question answering, have been propelled by the extensive computational resources and methodologies rooted in big data. Despite the considerable progress in NLP and Machine Learning, these advancements have not been seamlessly integrated into prevalent social platforms such as WhatsApp. This research introduces an inventive solution: a methodology empowering analyst to discern individuals' sentiments through semantic analysis, coupled with word clouds and sleep cycle patterns that visually represents the most frequently used words and their sleep patterns.Our approach leverages state-of-the-art NLP techniques, including advanced word embedding methodologies and knowledge-based graphs, to establish a system proficient in comprehending human behavior based on textual communication. Distinguished from existing AI tools, our system not only produces accurate summaries but also generates rich and profound insights into the original content. This depth is achieved through sophisticated linguistic analysis, surpassing the capabilities of tools like Chat GPT. Moreover, our system transcends language barriers, facilitating the traversal of multilingual data and enhancing accessibility on a global scale. In the quest to comprehend human behavior, it is imperative to gauge the temporal aspect, including the amount of time individuals spend on the application. This temporal understanding contributes significantly to discerning their personality types.