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Abstract
A wide range of data is now easily accessible via the microblogging service Twitter thanks to the rapid advancement of technology. The Bjorka controversy, one of the most talked-about topics right now, has generated numerous comments from the general public and thus has risen to the top. The Bjorka phenomenon is an obvious example of cybercrime, with a sharp uptick in incidents occurring in Indonesia during the COVID-19 pandemic. Sentiment analysis employing the Support Vector Machine technique allows for the statistical analysis of public opinion about Bjorka as it appears on the Twitter social network. Latent Dirichlet Allocation (LDA) will be used to analyze the sentiment analysis with SVM results, which have been separated into positive and negative sentiments. In this study, using LDA for sentiment analysis resulted in an accuracy of 89.5%. Dismantling government data, including personal data and government crimes, was the most positively predicted topic, with 75.2% of all predictions leaning in that direction. It is hoped that the government will be able to use the information gleaned from this study to better understand the public’s perspective and the trust deficits that need to be addressed
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References
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References
Anger, I., & Kittl, C. (2011). Measuring Influence on Twitter. Proceedings of the 11th international conference on knowledge managemet and knowledge technolo-gies.
DetikNews. (2022). Memaknai Anomali Respons Publik terhadap ”Hacker” Bjorka. Retrieved from https://news.detik.com/kolom/d-6306007/memaknai-anomali-respons-publik-terhadap-hacker-bjorka
CNN Indonesia. (2022). Retrieved from https://www.cnnindonesia.com/teknologi/-20220911160859-192-846286/drone-emprit-bjorka-jadi-perbincangan-terpopuler-kalahkan-banjir
Kominfo. (2022). Indonesia Peringkat ke-2 Dunia Kasus Kejahatan Siber. Re-trieved from https://www.kominfo.go.id/index.php/content/detail/4698/Indonesia-Peringkat-ke-2-Dunia-Kasus-Kejahatan-Siber/0/sorotan media
Jardim, S., & Mora, C. (2022). Customer reviews sentiment-based analysis and clustering for market-oriented tourism services and products develompment or positioning. Procedia Computer Science.
Al Amrani , Y., Lazaar, M., & El Kadiri, K. (2018). Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis. Procedia Com-puter Science.
Dey, S., Wasif, S., Tonmoy, D., & Sultana, S. (2020). A Comparative Study of Support Vector Machine and Na¨ıve Bayes Classifier for Sentiment Analysis on Amazon Product Reviews. International Conference on Contemporary Comput-ing and Applications (IC3A).
Sarkar, K., & Bhowmick, M. (2017). Sentiment Polarity Detection in Bengali Tweets Using Multinomial Na¨ıve Bayes and Support Vector Machine . IEEE Calcutta Conference (CALCON).
Ferdiana, R., Jatmiko, F., Purwanti, D. D., Ayu, A. S. T., & Dicka, W. F. (2019). Dataset Indonesia untuk analisis sentimen. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), 8(4), 334-339.
M. B. Mutanga and A. Abayomi, “Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach,” African J. Sci. Technol. Innov. Dev., vol. 0, no. 0, pp.1–10, 2020, doi:10.1080/20421338.2020.1817262.
R. Rani and D. K. Lobiyal, “An extractive text summarization approach using tagged-LDA based topic modeling,” Multimed. Tools Appl., vol. 80, no. 3, pp. 3275–3305, 2021, doi: 10.1007/s11042-020 09549-3.
S. Bellaouar, M. M. Bellaouar, and I. E. Ghada, “Topic modeling: Comparison of LSA and LDA on scientific publications,” ACM Int. Conf. Proceeding Ser., pp. 59–64, 2021, doi:10.1145/3456146.3456156.
Blei, D. Probabilistic Topic Models, Communications of the ACM, 2012, Vol 55, No.4.
V. K. Garbhapu, “A comparative analysis of Latent Semantic analysis and Latent Dirichlet allocation topic modeling methods using Bible data,” Indian J. Sci. Technol., vol. 13, no. 44, pp. 4474–4482, 2020, doi: 10.17485/ijst/v13i44.1479
H. P. Suresha and K. Kumar Tiwari, “Topic Modeling and Sentiment Analysis of Electric Vehicles of Twitter Data,” Asian J. Res. Comput. Sci., no. October, pp. 13–29, 2021, doi: 10.9734/ajrcos/2021/v12i230278.
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machine. Cambridge: Cambridge University Press.
Prasetyo, E. Data Mining: Konsep dan Aplikasi Menggunakan MAT- LAB.Yogyakarta.C.V Andi Offset. (2012)
M. Yekkekhany, G., Safari, A., Homayouni, S., and Hasanlou, “A Comparison study of different kernel functions for SVM-based classification of multi-temporal polametry SAR data,” Volume XL2W3, 2014.
Blei DM, Ng AY, Jordan MI. 2003. Latent Dirichlet allocation. J Mach Learn Res. 3(4–5):993–1022.
Kim M, Park Y, Yoon J. 2016. Generating patent development maps for technology monitoring using semantic patent-topic analysis. Comput Ind Eng. 98:289–299. doi:10.1016/j.cie.2016.06.006.
Lafia S, Kuhn W, Caylor K, Hemphill L. 2021. Mapping research topics at multiple levels of detail. Patterns. 2(3):100210. doi:10.1016/j.patter.2021.100210
R¨oder M, Both A, Hinneburg A. 2015. Exploring the Space of Topic Coherence Measures. Di dalam: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM. hlm 399–408.
Huang, L., Ma, J., & Chen, C. (2017, December). Topic detection from microblogs using T-LDA and perplexity. In 2017 24th Asia-Pacific software engineering conference workshops (APSECW) (pp. 71-77). IEEE.
Tijare, P., & Rani, P. J. (2020, December). Exploring popular topic models. In Journal of Physics: Conference Series (Vol. 1706, No. 1, p. 012171). IOP Publishing.
Du, B. X., & Liu, G. Y. (2021). Topic Analysis in LDA Based on Keywords Selection. Journal of Computers, 32(3), 1-12.
Lim, K. W., Chen, C., & Buntine, W. (2016). Twitter-network topic model: A full Bayesian treatment for social network and text modeling. arXiv preprint arXiv:1609.06791.
Rieger, J. (2020). ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations. Journal of Open Source Software, 5(51), 2181.