Latent Dirichlet Allocation (LDA) Topic Modeling and Sentiment Analysis for Myanmar Coup Tweets

Myanmar's coup in 2021 has produced public attention and discussions. Studying the trends and patterns of this event, a technological approach such as Natural Language Processing (NLP) could be used to analyze text data and develop insights. It can classify keywords and also group them into relevant topics, and then highlight aspects of the coup. Latent Dirichlet Allocation (LDA) is an NLP method that is widely used for information extraction and text classification. This paper focuses on the discussion about the utilization of LDA models in classifying the Topic Modeling for the N-best topic related to Myanmar's coup tweets data. This study also discussed the sentiment analysis model to visualize the trends and patterns related to public opinion based on the classified topics. The results showed the best-classified topics are 12 topics with the highest coherence score of 4.448, and these topics also can be grouped into three big themes and showed several negative sentiments for specific identified topics. To analyze further, the ANOVA test has been used to analyze further the patterns and trends of this public opinion based on the classified topics and sentiment scores