Ready to submit?

Start a new submission or continue a submission in progress

Go to submission page Instructions for authors Your submissions
Journal Description Journal History Editorial Team Contact Privacy Statement Copyright Notice
Current Issue Past Issues

Mapping Public Opinion Topics on Educational Policy on Twitter Based on Geospatial Aspects Using Latent Dirichlet Allocation (LDA)

Authors

  • Muharoma Fahnur Ihsandi

    UIN Sulthan Thaha Saifuddin Jambi
    Author

  • Shalsa Rhamadani

    Author

  • Urwawuska Ladini

    Author
Keywords:
topic modelling, latent Dirichlet allocation, public opinion, education policy, text mining
Abstract

Educational policy has emerged as a prominent public issue that is widely discussed across social media platforms. As an open-access microblogging platform, Twitter generates a substantial volume of user-generated content that reflects public opinion in real time. Such data provide valuable insights for understanding societal responses to government policies. This study aims to examine the topic segmentation of public opinion regarding educational policies in Indonesia by employing the Latent Dirichlet Allocation (LDA) model. The dataset comprised 8,030 Indonesian-language tweets collected using education-related keywords. After a relevance filtering process, 699 tweets were retained for analysis. The text preprocessing procedures included case folding, removal of numerical characters and punctuation marks, elimination of Indonesian stopwords, and stemming to normalize word forms. The cleaned corpus was then transformed into a Document–Term Matrix (DTM) representation prior to topic modeling. LDA was applied with three predefined topics to extract latent thematic structures within the dataset. The results reveal that public discourse can be categorized into three principal themes: (1) government policies and national conditions, (2) government performance and public policy implementation, and (3) higher education issues related to university students. The topic distribution indicates that discussions concerning government policy and higher education issues are the most dominant themes within public conversations on Twitter. These findings contribute to the growing body of research on social media analytics in public policy studies and provide empirical evidence that may assist policymakers in identifying public concerns and evaluating policy communication strategies

References

Arianto, & Anuraga. (2025). Topic modeling analysis of Indonesian policy discourse. Journal of Data Science and Analytics.

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. https://doi.org/10.1145/2133806.2133826

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

Egger, R., & Yu, J. (2022). A topic modeling comparison between LDA, NMF, Top2Vec, and BERTopic to demystify Twitter posts. Frontiers in Sociology, 7, 886498. https://doi.org/10.3389/fsoc.2022.886498

Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2, Article 5. https://doi.org/10.1186/s40537-015-0015-2

Göçen, A., Ibrahim, M. M., & Khan, A. U. I. (2024). Public attitudes toward higher education using sentiment analysis and topic modeling. Discover Artificial Intelligence, 4, Article 83. https://doi.org/10.1007/s44163-024-00195-4

Hu, Y., John, A., Wang, F., Seligmann, D. D., & Kambhampati, S. (2012). ET-LDA: Joint topic modeling for analyzing Twitter feeds. ACM Transactions on Intelligent Systems and Technology, 4(2). https://doi.org/10.1145/2661829.2662005

Jungherr, A. (2016). Twitter use in election campaigns: A systematic literature review. Journal of Information Technology & Politics, 13(1), 72–91. https://doi.org/10.1080/19331681.2015.1132401

Mujahid, M., Lee, E., Rustam, F., Washington, P. B., Ullah, S., Reshi, A. A., & Ashraf, I. (2021). Sentiment analysis and topic modeling on tweets about online education during COVID-19. Applied Sciences, 11(18), 8438. https://doi.org/10.3390/app11188438

Negara, E. S., & Triadi, D. (2022). Topic modeling using LDA on Twitter data with Indonesia keyword. Bulletin of Social Informatics Theory and Application, 5(2). https://doi.org/10.31763/businta.v5i2.455

Patmawati, & Yusuf. (2021). Topic modeling analysis on Indonesian public policy tweets. Journal of Data and Information Science.

Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., & Acharya, U. R. (2023). Sentiment analysis and opinion mining on educational data. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10260-y

Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. O’Reilly Media.

Sokolova, M., & Bobicev, V. (2016). Topic modeling for Twitter event analysis. Information Processing & Management, 52(6). https://doi.org/10.1016/j.ipm.2016.02.005

Sun, J., & Yan, L. (2023). Using topic modeling to understand comments in student evaluations of teaching. Discover Education, 2, Article 25. https://doi.org/10.1007/s44217-023-00051-0

Theocharis, Y., Barberá, P., Fazekas, Z., Popa, S., & Parnet, O. (2021). A bad workman blames his tweets. Journal of Communication, 71(5), 636–661. https://doi.org/10.1093/joc/jqab034

Uthirapathy, S. E., & Sandanam, D. (2023). Topic modelling and opinion analysis on climate change tweets. Procedia Computer Science, 218, 1544–1553. https://doi.org/10.1016/j.procs.2023.01.134

Wang, H., et al. (2022). Tweet topics and sentiments relating to distance learning. Scientific Reports, 12, Article 12915. https://doi.org/10.1038/s41598-022-12915-w

Wang, X., McCallum, A., & Wei, X. (2007). Topical n-grams: Phrase and topic discovery. Proceedings of EMNLP. https://doi.org/10.3115/1610075.1610094

Zulqarnain, M. I., & Cahyo, P. W. (2023). Topic modeling and social network analysis on stock discussion tweets. Indonesian Journal on Data Science. https://doi.org/10.33096/ijodas.v1i1.15

Cover Image
Downloads
Published
2026-02-28
Issue
Vol. 1 No. 1 (2026): GeoHuman: People, Place, and Power
Section
Articles
License

Copyright (c) 2026 GeoHuman: People, Place, and Power

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mapping Public Opinion Topics on Educational Policy on Twitter Based on Geospatial Aspects Using Latent Dirichlet Allocation (LDA). (2026). GeoHuman: People, Place, and Power, 1(1), 53-61. https://geohuman.bumi-spasial.com/index.php/Journal/article/view/12

Similar Articles

You may also start an advanced similarity search for this article.