Topic Modelling in COVID-19 Vaccination Refusal Cases Using Latent Dirichlet Allocation and Latent Semantic Analysis
Pemodelan Topik Pada Kasus Tolak Vaksinasi COVID-19 Menggunakan Latent Dirichlet Allocation dan Latent Semantic Analysis
DOI:
https://doi.org/10.21070/ups.774Keywords:
Text Mining, LDA, LSA, Topic Modeling, Pemodelan Topik, COVID-19, Vaksin, Anti Vaksin, Twitter, Data Twitter, Scrapping, Crawling, Big Data, WordcloudAbstract
The COVID-19 vaccination program in Indonesia goes hand in hand with issues that are circulating, causing controversy and rejection of vaccination on social media, especially Twitter. There are many factors that influence vaccine rejection on Twitter, to summarize frequently discussed topics and find out hidden topics, this study uses the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) methods. The topics that appear will be further analyzed through coherence score by applying a limit of 20 topics to display the best value. Further modeling experiments are carried out to display topics through LDA and LSA models, this study takes 6 topics with highest coherence values including the right of individuals to choose whether to be vaccinated or not (0.484607), the Ribka Tjiptaning controversy (0.473368), rejection of the COVID-19 vaccine by groups represented by public figures (0.463631), punishment for non-compliance in the form of fines (0.324924), and halal certification (0.312521).
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