Sentiment Analysis and Topic Modeling of Twitter Conversations in Indonesia's 2024 Presidential Election
Article Sidebar
Read Counter : 2
Download : 0
Main Article Content
Abstract
The 2024 Indonesian presidential election has garnered significant attention owing to the increasing influence of social media on public perceptions of political leaders and information exchange. This study aimed to analyse the online discourse surrounding three prominent Indonesian presidential candidates: Anies Baswedan, Prabowo Subianto, and Ganjar Pranowo. Utilising sentiment analysis, frequency analysis, and the Latent Dirichlet Allocation (LDA) algorithm, this study examines conversations on social media platforms, focusing on word frequency, sentiment, subjects, and entities that emerge in the discussions. The findings reveal that Prabowo Subianto is the most frequently mentioned candidate, followed by Anies Baswedan, and Ganjar Pranowo. Sentiment analysis indicated a predominantly neutral to slightly positive sentiment across conversations. The LDA topic analysis uncovered distinct campaign focuses for each candidate, with Anies emphasising local issues and debates, Prabowo concentrating on national discourse and defense policy, and Ganjar discussing national concerns, justice, and progress. Named Entity Recognition (NER) highlights the prominence of entities such as "Indonesia”, "Prabowo”, and "Pilpres2024" in online discussions. This study underscores the crucial role of social media in shaping public opinion and provides valuable insights into the online narratives of presidential candidates. These findings contribute to a deeper understanding of the dynamics of digital elections and offer guidance for political analysts and academics in navigating the evolving landscape of online political discourse in Indonesia.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The proposed policy for journals that offer open access
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Abdillah, L. A. (2014). Indonesian’s presidential social media campaigns. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1409.8372
Adamik-Szysiak, M. (2019). SOCIAL MEDIA AS A TOOL OF POLITICAL PERMANENT CAMPAIGN ON THE EXAMPLE OF THE ACTIVITY OF POLISH POLITICIANS. https://doi.org/10.35603/ssa2019/issue2.01
Ahlf, M., & McNeil, S. (2023a). A Systematic Review of Research on Moderators in Asynchronous Online Discussions. Online Learning Journal, 27(1), 219–262. https://doi.org/10.24059/olj.v27i1.3381
Ahlf, M., & McNeil, S. G. (2023b). An exploratory review of literature on moderation in asynchronous discussions. Distance Education, 44(1), 137–161. https://doi.org/10.1080/01587919.2022.2150146
Alamsyah, A., Rochmah, W. Y., & Nurnafia, A. N. (2021). Deciphering Social Opinion Polarization Towards Political Event Based on Content and Structural Analysis. ArXiv (Cornell University). https://arxiv.org/pdf/2102.08249v1.pdf
Aliyu, M. K., Ikedinma, H. A., Livinus, N., & Olanrewaju, S. R. (2019). Public Expectations from Political Office Holders on Good Governance in Oyo State, Nigeria. https://doi.org/10.5539/jpl.v12n3p136
Alvinto, B., Irawati, Rusta, A., & Fajri, M. (2023). The Influence of Social Media on People’s Political Behavior. JOELS, 4(2), 89–95. https://doi.org/10.31849/joels.v4i2.13057
Benoit, W. L., & Glantz, M. J. (2020). Presidential campaigns in the age of social media: Clinton and trump. In Presidential Campaigns in the Age of Social Media: Clinton and Trump. International Academic Publishers. https://doi.org/10.3726/b16098
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Bolsen, T., & Thornton, J. R. (2021). Candidate and party affective polarization in U.S. presidential elections: The person-negativity bias? Electoral Studies, 71. https://doi.org/10.1016/j.electstud.2021.102293
Bruns, A., & Stieglitz, S. (2013). Quantitative Approaches to Comparing Communication Patterns on Twitter. Journal of Technology in Human Services, 31(2), 160–185.
Budiharto, W., & Meiliana, M. (2018). Prediction and analysis of Indonesia Presidential election from Twitter using sentiment analysis. Journal of Big Data, 5(1). https://doi.org/10.1186/s40537-018-0164-1
Budiono, D. F., Nugroho, A. S., & Doewes, A. (2017). Twitter sentiment analysis of DKI Jakarta’s gubernatorial election 2017 with predictive and descriptive approaches. https://doi.org/10.1109/ic3ina.2017.8251746
Cairo, A. (n.d.). The Functional Art: An Introduction to Information Graphics and Visualization. New Riders.
de Oliveira Dias, F., & de Miranda Rocha, G. (2023). Territorial Development: A Bibliometric Analysis of Scientific Production Through RStudio® and VOSviewer® (2013-2022). https://doi.org/10.24857/rgsa.v18n2-045
Demillo, R. E., Solano, G., & Oco, N. (2023). Philippine National Elections 2022: Voter Preferences and Topics of Discussion on Twitter. 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023, 724–729. https://doi.org/10.1109/ICAIIC57133.2023.10067082
Dikiyanti, T. D., Rukmi, A. M., & Irawan, M. I. (2021). Sentiment analysis and topic modeling of BPJS Kesehatan based on twitter crawling data using Indonesian Sentiment Lexicon and Latent Dirichlet Allocation algorithm. Journal of Physics: Conference Series, 1821(1), 12054. https://doi.org/10.1088/1742-6596/1821/1/012054
DiMaggio, P., Nag, M., & Blei, D. (2013). Exploiting Affinities between Topic Modeling and the Sociological Perspective on Culture. Poetics, 41(6), 570–606.
Dimitrova, D. V., Shehata, A., Strömbäck, J., & Nord, L. W. (2011). The Effects of Digital Media on Political Knowledge and Participation in Election Campaigns. https://journals.sagepub.com/doi/10.1177/0093650211426004
Gabbriellini, S. (2014). The evolution of online forums as communication networks: An agent-based model. Revue Francaise de Sociologie, 55(4), 805–826. https://doi.org/10.3917/rfs.554.0805
Gautrais, C., Cellier, P., Quiniou, R., & Termier, A. (2017). Topic signatures in political campaign speeches. EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings, 2342–2347. https://doi.org/10.18653/v1/d17-1249
Ghadirian, H., Salehi, K., & Ayub, A. F. M. (2019). Assessing the effectiveness of role assignment on improving students’ asynchronous online discussion participation. International Journal of Distance Education Technologies, 17(1), 31–51. https://doi.org/10.4018/IJDET.2019010103
Gibson, A. (2019). Free Speech and Safe Spaces: How Moderation Policies Shape Online Discussion Spaces. Social Media and Society, 5(1). https://doi.org/10.1177/2056305119832588
Hadma, A. M., & Anggoro, J. D. (2021). Political communication in the age of social media. COMMICAST, 3(1), 1–7. https://doi.org/10.12928/commicast.v3i1.5114
Helmi, A. F., Riasetiawan, M., Priwati, A. R., Rahma, I. M., Arlianto, A., Marvianto, R. D., & Shaleha, R. R. A. (2020). Hashtag war: 2019 Presidential election rhetoric in Indonesia. Humanitas, 17(2), 140. https://doi.org/10.26555/humanitas.v17i2.17045
Hemmings-Jarrett, K., Jarrett, J., & Blake, M. B. (2017). Evaluation of User Engagement on Social Media to Leverage Active and Passive Communication. In M. P.P. & C. W. (Eds.), Proceedings - 2017 IEEE 1st International Conference on Cognitive Computing, ICCC 2017 (pp. 132–135). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IEEE.ICCC.2017.24
Hidayatullah, A. F., Aditya, S. K., Karimah, & Gardini, S. T. (2019). Topic modeling of weather and climate condition on twitter using latent dirichlet allocation (LDA). IOP Conference Series: Materials Science and Engineering, 482, 12033. https://doi.org/10.1088/1757-899x/482/1/012033
Hillman, T., Lundin, M., Rensfeldt, A. B., Lantz-Andersson, A., & Peterson, L. (2021). Moderating professional learning on social media - A balance between monitoring, facilitation and expert membership. Computers and Education, 168. https://doi.org/10.1016/j.compedu.2021.104191
Jun Hyun Ryoo, J., & Bendle, N. (2017). Understanding the social media strategies of U.S. primary candidates. Journal of Political Marketing, 16(3–4), 244–266. https://doi.org/10.1080/15377857.2017.1338207
Jungherr, A. (2016). Analyzing Political Communication with Digital Trace Data. Springer.
Karolita, D., & Lestari, A. (2019). Analyzing Netizens’ Perceptions Towards Indonesian Presidential Candidates Using Topic Modeling Approach. IOP Conference Series: Materials Science and Engineering, 546(5), 52037. https://doi.org/10.1088/1757-899x/546/5/052037
Krippendorff, K. (n.d.). Content Analysis: An Introduction to Its Methodology. Sage.
Le, H. T., Boynton, G. R., Mejova, Y., Shafiq, Z., & Srinivasan, P. (2017). Revisiting the American voter on Twitter. Conference on Human Factors in Computing Systems - Proceedings, 2017-May, 4507–4519. https://doi.org/10.1145/3025453.3025543
Loper, E., & Bird, S. (2002). NLTK: The Natural Language Toolkit. Proceedings of the ACL-02 Workshop.
Mano, M., Dalle, J.-M., & Tomasik, J. (2017). An author network to classify open online discussions. Proceedings of the 13th International Symposium on Open Collaboration, OpenSym 2017. https://doi.org/10.1145/3125433.3125450
Medaglia, R., & Yang, Y. (2017). Online public deliberation in China: evolution of interaction patterns and network homophily in the Tianya discussion forum. Information Communication and Society, 20(5), 733–753. https://doi.org/10.1080/1369118X.2016.1203974
Na, H. J., Lee, K. C., & Kim, S.-T. (2021). Integrating text-mining and balanced scorecard techniques to investigate the association between ceo message of homepage words and financial status: Emphasis on hospitals. Healthcare (Switzerland), 9(4), 408. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104305967&doi=10.3390%2Fhealthcare9040408&partnerID=40&md5=252c6c48d1b27389243f36f6e27f7a50
Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Lingvisticae Investigationes, 30(1), 3–26.
Nausheen, F., & Begum, S. H. (2018). Sentiment analysis to predict election results using Python. Proceedings of the 2nd International Conference on Inventive Systems and Control, ICISC 2018, 1259–1262. https://doi.org/10.1109/ICISC.2018.8399007
Newman, N., Fletcher, R., Schulz, A., Andı, S., & Nielsen, R. K. (2021). Reuters Institute Digital News Report 2021. Reuters Institute for the Study of Journalism.
Olaniran, B., & Williams, I. M. (2020). Social Media Effects: Hijacking Democracy and Civility in Civic Engagement. In Springer eBooks (pp. 77–94). https://doi.org/10.1007/978-3-030-36525-7_5
Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. In Foundations and Trends in Information Retrieval (Vol. 2, Issues 1–2).
Priyanto, A., Pribadi, U., & Rahmanto, F. (2021). The Effect of Political Party Communication Through Social Media on The Results of The 2019 Legislative General Election (Nasdem Party, PKS, and PDIP Twitter Case Study). IOP Conference Series: Earth and Environmental Science, 717(1), 12022. https://doi.org/10.1088/1755-1315/717/1/012022
Purbolaksono, A. (2020). The Use of Social Media as Candidate Campaign Tool in elections in Indonesia Post-New Order. https://doi.org/10.2991/icstcsd-19.2020.14
Rahutomo, R., Budiarto, A., Purwandari, K., Perbangsa, A. S., Cenggoro, T. W., & Pardamean, B. (2020). Ten-Year Compilation of #SaveKPK Twitter Dataset. https://doi.org/10.1109/icimtech50083.2020.9211246
Roos, C. A., Utz, S., Koudenburg, N., & Postmes, T. (2023). Diplomacy online: A case of mistaking broadcasting for dialogue. European Journal of Social Psychology. https://doi.org/10.1002/ejsp.3015
Rossini, P. G. C., Zhang, F., Hemsley, J., Robinson, J., Tanupabrungsun, S., & Stromer-Galley, J. (2017). Social media, U.S. Presidential campaigns, and public opinion polls: Disentangling effects. ACM International Conference Proceeding Series, Part F1296. https://doi.org/10.1145/3097286.3097342
Rossini, P., Hemsley, J., Tanupabrungsun, S., Zhang, F., & Stromer-Galley, J. (2018). Social Media, Opinion Polls, and the Use of Persuasive Messages During the 2016 US Election Primaries. Social Media and Society, 4(3). https://doi.org/10.1177/2056305118784774
Sandu, A.-M., Cotfas, L., Delcea, C., Crăciun, L., & Molănescu, A. G. (2023). Sentiment Analysis in the Age of COVID-19: A Bibliometric Perspective. Information, 14(12), 659. https://doi.org/https://doi.org/10.3390/info14120659
Shirky C. (2010). [PDF] The political power of social media: Technology, the public sphere, and political change | Semantic Scholar. https://www.semanticscholar.org/paper/The-political-power-of-social-media%3A-Technology%2C-Shirky/73f29492478bb28c422d32f075f688a903addec1
Sukmayadi, V., & Effendi, R. (2018). Social Media Emotion in Politics: An Indonesian Case Study of Political Environment on Facebook. IOP Conference Series: Earth and Environmental Science, 145, 12009. https://doi.org/10.1088/1755-1315/145/1/012009
Tucker, P. D., & Smith, S. S. (2021). Changes in Candidate Evaluations over the Campaign Season: A Comparison of House, Senate, and Presidential Races. Political Behavior, 43(4), 1639–1661. https://doi.org/10.1007/s11109-020-09603-8
Tufte, E. R. (2001). The Visual Display of Quantitative Information. In Graphics Press.
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2010). Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media.
Ulfa, K., Purnomo, E., & Kasiwi, A. N. (2020). The Campaign Strategy of 2019 Presidential and Vice-Presidential Elections on Social Media. Society, 8, 284–297. https://doi.org/10.33019/society.v8i2.137
Valenzuela, S., Halpern, D., Katz, J. E., & Miranda, J. P. (2019). The Paradox of Participation Versus Misinformation: Social Media, Political Engagement, and the Spread of Misinformation. https://doi.org/10.1080/21670811.2019.1623701
Winarko, E., & Pulungan, R. (2019). Trending topics detection of Indonesian tweets using BN-grams and Doc-p. Journal of King Saud University - Computer and Information Sciences, 31(2), 266–274. https://doi.org/10.1016/j.jksuci.2018.01.005
Xu, X., & Plaks, J. E. (2023). Aspect-Level Personality Characteristics of U.S. Presidential Candidate Supporters in the 2016 and 2020 Elections. Social Psychological and Personality Science, 14(5), 588–598. https://doi.org/10.1177/19485506221113954
Yang, Y. (2019). When power goes wild online: How did a voluntary moderator’s abuse of power affect an online community? Proceedings of the Association for Information Science and Technology, 56(1), 504–508. https://doi.org/10.1002/pra2.55