LATENT DIRICHLET ALLOCATION (LDA) METHOD ANALYSIS ABOUT COVID-19 VACCINE ON TWITTER SOCIAL MEDIA

  • Happy Alyzhya Haay Department of Mathematics and Data Science, Faculty of Science and Mathematics, Universitas Kristen Satya Wacana
  • Adi Setiawan Department of Mathematics and Data Science, Faculty of Science and Mathematics, Universitas Kristen Satya Wacana
DOI: https://doi.org/10.30598/barekengvol16iss1pp189-196
Keywords: COVID-19, Latent Dirichlet Allocation (LDA), Twitter

Abstract

Twitter is one social media that often provides much information for its users, one of which is information regarding the COVID-19 vaccination. This study aimed to explore and find out what topics are often discussed on Twitter social media. One of which is the topic of COVID-19 vaccination using the Latent Dirichlet Allocation (LDA) method and analysis of the frequency of keywords that often appear with this topic. The Tweet data used in this study was taken from Twitter users worldwide in November 2021. In this study, the results of sentiment analysis were obtained from the tweet data taken, which was divided into positive sentiment and negative sentiment, namely "vaccination" with 40 words and "'Covid19" with 35 words

Downloads

Download data is not yet available.

References

Levani, Prastya, and Mawaddatunnadila, “Coronavirus Disease 2019 (COVID-19): Patogenesis, Manifestasi Klinis dan Pilihan Terapi,” J. Kedokt. dan Kesehat., vol. 17, no. 1, pp. 44–57, 2021, [Online]. Available: https://jurnal.umj.ac.id/index.php/JKK/article/view/6340.

S. Syamaidzar, “Review Vaksin Covid-19,” Res. Gate, no. July, pp. 1–15, 2020.

F. Faulin Nur and V. N. Rahman, “Penyuluhan Program Vaksinasi Covid-19 Pada Mayarakat Desa Pakistaji,” vol. 03, no. 02, pp. 491–497, 2021.

F. Rachman, S. P.-I. of Health, and undefined 2020, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia tentang Vaksin COVID-19 pada Media Sosial Twitter,” ["Analysis of the Pros and Cons of the Indonesian Society's Sentiment regarding the COVID-19 Vaccine on Twitter Social Media,"] Inohim.Esaunggul.Ac.Id, vol. 8, no. 2, pp. 2655–9129, 2020, [Online]. Available: https://inohim.esaunggul.ac.id/index.php/INO/article/download/223/175.

U. T. Setijohatmo, S. Rachmat, T. Susilawati, Y. Rahman, and K. Kunci, “Analisis Metoda Latent Dirichlet Allocation untuk Klasifikasi Dokumen Laporan Tugas Akhir Berdasarkan Pemodelan Topik,” ["Analysis of Latent Dirichlet Allocation Method for Classification of Final Project Report Documents Based on Topic Modeling,"] Pros. 11th Ind. Res. Work. Natl. Semin., pp. 402–408, 2020.

Y. U. Al-khairi, Y. Wibisono, and B. L. Putro, “Deteksi Topik Fashion Pada Twitter Dengan Latent Dirichlet Allocation,” ["Fashion Topic Detection on Twitter with Latent Dirichlet Allocation,"] J. Apl. dan Teor. Ilmu Komput., vol. 1, no. 1, pp. 1–10, 2017, doi: 10.31227/osf.io/9twmn.

P. Dellia and A. Tjahyanto, “Tax Complaints Classification on Twitter Using Text Mining,” IPTEK J. Sci., vol. 2, no. 1, p. 11, 2017, doi: 10.12962/j23378530.v2i1.a2254.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of Twitter social media with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

A. Gupta and R. Katarya, “PAN-LDA: A latent Dirichlet allocation based novel feature extraction model for COVID-19 data using machine learning,” Comput. Biol. Med., vol. 138, no. July, p. 104920, 2021, doi: 10.1016/j.compbiomed.2021.104920.

K. M. Kwayu, V. Kwigizile, K. Lee, J.-S. Oh, and T. Nelson, “Automatic topics extraction from crowdsourced cyclists near-miss and collision reports using text mining and Artificial Neural Networks,” Int. J. Transp. Sci. Technol., no. October, 2021, doi: 10.1016/j.ijtst.2021.10.005.

S. Kumar, A. K. Kar, and P. V. Ilavarasan, “Applications of text mining in services management: A systematic literature review,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100008, 2021, doi: 10.1016/j.jjimei.2021.100008.

B. W. Arianto and G. Anuraga, “Topic Modeling for Twitter Users Regarding the ‘Ruanggguru’ Application,” J. ILMU DASAR, vol. 21, no. 2, p. 149, 2020, doi: 10.19184/jid.v21i2.17112.

M. Sakiyama, N. Fujii, D. Kokuryo, and T. Kaihara, “Visualization of group discussion using correspondence analysis and LDA in Ideathon,” Procedia CIRP, vol. 88, pp. 595–599, 2020, doi: 10.1016/j.procir.2020.05.104.

Zulhanif, “Pemodelan Topik Dengan Latent Dirichlet Allocation,” Semin. Nas. Pendidik. Mat., pp. 1–8, 2016.

T. Williams and J. Betak, “A Comparison of LSA and LDA for the Analysis of Railroad Accident Text,” Procedia Comput. Sci., vol. 130, pp. 98–102, 2018, doi: 10.1016/j.procs.2018.04.017.

C. Liu et al., “Improving sentiment analysis accuracy with emoji embedding,” J. Saf. Sci. Resil., vol. 2, no. 4, pp. 246–252, 2021, doi: 10.1016/j.jnlssr.2021.10.003.

J. Li, D. Lowe, L. Wayment, and Q. Huang, “Text mining datasets of β-hydroxybutyrate (BHB) supplement products’ consumer online reviews,” Data Br., vol. 30, 2020, doi: 10.1016/j.dib.2020.105385.

N. Chintalapudi, G. Battineni, M. Di Canio, G. G. Sagaro, and F. Amenta, “Text mining with sentiment analysis on seafarers’ medical documents,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 1, p. 100005, 2021, doi: 10.1016/j.jjimei.2020.100005.

Z. Xu and J. Zhang, “Extracting Keywords from Texts based on Word Frequency and Association Features,” Procedia Comput. Sci., vol. 187, pp. 77–82, 2021, doi: 10.1016/j.procs.2021.04.035.

J. Y. Park, E. Mistur, D. Kim, Y. Mo, and R. Hoefer, “Toward human-centric urban infrastructure: Text mining for social media data to identify the public perception of COVID-19 policy in transportation hubs,” Sustain. Cities Soc., vol. 76, no. October 2021, p. 103524, 2022, doi: 10.1016/j.scs.2021.103524.

D. Obembe, O. Kolade, F. Obembe, A. Owoseni, and O. Mafimisebi, “Covid-19 and the tourism industry: An early stage sentiment analysis of the impact of social media and stakeholder communication,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100040, 2021, doi: 10.1016/j.jjimei.2021.100040.

N. Zhang, R. Liu, X.-Y. Zhang, and Z.-L. Pang, “The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining,” Data Sci. Manag., vol. 3, no. September, pp. 22–32, 2021, doi: 10.1016/j.dsm.2021.09.001.

P. Aliandu, “Sentiment Analysis to Determine Accommodation, Shopping and Culinary Location on Foursquare in Kupang City,” Procedia Comput. Sci., vol. 72, pp. 300–305, 2015, doi: 10.1016/j.procs.2015.12.144.

Published
2022-03-21
How to Cite
[1]
H. Haay and A. Setiawan, “LATENT DIRICHLET ALLOCATION (LDA) METHOD ANALYSIS ABOUT COVID-19 VACCINE ON TWITTER SOCIAL MEDIA”, BAREKENG: J. Math. & App., vol. 16, no. 1, pp. 189-198, Mar. 2022.
Issue
Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.