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@article{174217,
        author = {Arya Chougle and Shrutika Patil and Avinash Ghugwad and Akshay Gaikwad and Prof.Sheetal Patil},
        title = {Whatsapp Chat Analyzer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {3117-3127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174217},
        abstract = {This research paper presents a comprehensive analysis of WhatsApp chat data utilizing machine learning techniques. The study focuses on extracting valuable insights such as total message count, media distribution, link sharing frequency, and emoji usage within the context of group conversations. Additionally, it investigates temporal trends through a monthly timeline visualization and identifies the most active participants using a bar graph representation. By employing natural language processing and data visualization methodologies, the study aims to provide a deeper understanding of communication patterns and dynamics within WhatsApp groups, contributing to the field of chat analytics and social network analysis.},
        keywords = {WhatsApp chat text file, NLP, Matplotlib, Seaborn Emoji analysis, Streamlit, etc.},
        month = {March},
        }