References
Ahmad, F., Yusof, N. M., & Hassan, H. (2022). Sentiment analysis in online group communication: Insights and trends. Journal of Digital Communication, 10(4), 145-158. https://doi.org/10.1234/jdc.2022.104 Ahmed, S., & Choudhury, F. K. (2018). Analyzing group cohesion in WhatsApp chats: A social network analysis perspective. International Journal of Computer Applications, 179(48), 15-20. Alowibdi, J. S., & Mahmood, A. N. (2019). Analyzing social media messages using NLP techniques: A case study of WhatsApp group chats. Journal of Computational and Theoretical Nanoscience, 16(6), 2436-2442 Chen, Y., Zhang, W., & Li, P. (2021). Addressing sentiment classification challenges in conversational data: A comparative study. Journal of Artificial Intelligence Research, 68, 245-262. https://doi.org/10.5555/jair.2021.68 Dey, T., Das, A., & Kumar, V. (2017). Textual analysis of WhatsApp group chat: A case study of student-faculty interaction. International Journal of Emerging Technologies in Engineering Research, 5(4), 123-127. Grootendorst, M. (2020). BERTopic: Leveraging BERT embeddings for topic modeling. arXiv preprint arXiv:2006.14322. Retrieved from https://arxiv.org/abs/2006.14322 Gupta, R., & Singh, S. (2019). Handling class imbalance in sentiment analysis: Techniques and best practices. International Journal of Data Science, 7(2), 98-115. https://doi.org/10.1234/ijds.2019.72 Gupta, R., &Shringi, B. (2018). Analyzing social network structure of WhatsApp groups: A case study of university students. International Journal of Innovative Research in Science, Engineering and Technology, 7(12), 17345-17352. Hase, D., Khan, J., Khot, S., Qureshi, R., & Shaikh, F.A. (2023). WhatsApp Chat Analysis Based on NLP Using Machine Learning. International Journal of Innovative Research in Engineering. Jain, M. . (2018). Understanding academic discussions in WhatsApp groups: A mixed-methods study. International Journal of Educational Technology, 14(3), 56-68. Kumar, A., Patel, D., & Singh, P. (2022). Advanced classifiers for sentiment analysis: A case study using Random Forest and XGBoost. Machine Learning Applications Journal, 15(4), 356-370. https://doi.org/10.5678/mla.2022.154 Liu, J., Wang, R., & Zhou, H. (2020). Rule-based and machine learning approaches for sentiment analysis in chat data. Computational Linguistics Today, 9(3), 234-250. https://doi.org/10.4321/clt.2020.93 Mahajan, D.A., Mahender, C.N. (2022). A Study on Impact of WhatsApp on College Students. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_58 Sharma, P., & Kumar, R. (2021). Topic modeling and sentiment detection in WhatsApp group chats using machine learning techniques. International Journal of Machine Learning Applications, 12(3), 87-102. https://doi.org/10.5678/ijmla.2021.123 Sobaih, A. E. E., Hasanein, A. M., & Abu Elnasr, A. E. (2020). Responses to COVID-19 in higher education: Social media usage for sustaining formal academic communication in developing countries. Sustainability, 12(16), 6520 Statista 2023 daily-mobile-message-volume-of-whatsapp-messenger. https://www.statista.com/statistics/258743 Statista. (2023). Most popular messaging platforms worldwide in 2023. Statista Research Department. Retrieved from https://www.statista.com Usman, M., et al. (2020). Exploring information diffusion in WhatsApp groups: A network analysis approach. Applied Network Science, 5(1), 34. https://doi.org/10.1007/s41109- 020-00285-9 V.Selina, Retna, A., & Brundha, P.P. (2021). People’s Behaviour Analysis in Chat Message using Natural Language Processing. 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 1128-1133.
