Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm
Keywords:Topic Analysis, LDA, Trending Twitter Topics, Twitter Conversation Topics
In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.
Ageed, Z. S., Zeebaree, S. R., Sadeeq, M. M., Kak, S. F., Yahia, H. S., Mahmood, M. R., & Ibrahim, I. M. (2021). Comprehensive survey of big data mining approaches in cloud systems. Qubahan Academic Journal, 1(2), 29-38.
Ayora, V., Horita, F., & Kamienski, C. (2021, January). Profiling Online Social Network Platforms: Twitter vs. Instagram. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 2792).
Carracedo, P., Puertas, R., & Marti, L. (2021). Research lines on the impact of the COVID-19 pandemic on business. A text mining analysis. Journal of Business Research, 132, 586-593.
Chauhan, U., & Shah, A. (2021). Topic modeling using latent Dirichlet allocation: A survey. ACM Computing Surveys (CSUR), 54(7), 1-35.
Ewieda, M., Shaaban, E. M., & Roushdy, M. (2021). Customer Retention: Detecting Churners in Telecoms Industry using Data Mining Techniques. International Journal of Advanced Computer Science and Applications, 12(3).
Fraiwan, M. (2022). Identification of markers and artificial intelligence-based classification of radical Twitter data. Applied Computing and Informatics.
Gupta, A., & Katarya, R. (2021). PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning. Computers in biology and medicine, 138, 104920.
Gurcan, F., Ozyurt, O., & Cagitay, N. E. (2021). Investigation of emerging trends in the e-learning field using latent Dirichlet allocation. International Review of Research in Open and Distributed Learning, 22(2), 1-18.
Haoxiang, W., & Smys, S. (2021). Big data analysis and perturbation using data mining algorithm. Journal of Soft Computing Paradigm (JSCP), 3(01), 19-28.
Haupt, J. (2021). Facebook futures: Mark Zuckerberg’s discursive construction of a better world. New Media & Society, 23(2), 237-257.
Hudaefi, F. A., Caraka, R. E., & Wahid, H. (2021). Zakat administration in times of COVID-19 pandemic in Indonesia: a knowledge discovery via text mining. International Journal of Islamic and Middle Eastern Finance and Management.
Irgashevich, S. T., Odilovich, O. A., & Mamadaliyevich, G. E. (2022). Internet Technologies In The Tourism Industry. Web of Scientist: International Scientific Research Journal, 3(9), 57-64.
Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008.
Ning, W., Liu, J., & Xiong, H. (2022). Knowledge discovery using an enhanced latent Dirichlet allocation-based clustering method for solving on-site assembly problems. Robotics and Computer-Integrated Manufacturing, 73, 102246.
Oatley, G. C. (2022). Themes in data mining, big data, and crime analytics. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 12(2), e1432.
Regin, R., Rajest, S. S., & Singh, B. (2021). Spatial data mining methods databases and statistics point of views. Innovations in Information and Communication Technology Series, 103-109.
Sharma, C., & Sharma, S. (2022). Latent DIRICHLET allocation (LDA) based information modelling on BLOCKCHAIN technology: a review of trends and research patterns used in integration. Multimedia Tools and Applications, 1-27.
Valkenburg, P. M., Meier, A., & Beyens, I. (2022). Social media use and its impact on adolescent mental health: An umbrella review of the evidence. Current opinion in psychology, 44, 58-68.
Yatabe, J., Yatabe, M. S., & Ichihara, A. (2021). The current state and future of internet technology-based hypertension management in Japan. Hypertension Research, 44(3), 276-285.