Topic Modeling Approach on Twitter Data Relevant to Political Changes During the COVID-19 Pandemic

The COVID-19 pandemic has affected various sectors of society, including politics. The political changes have had both positive and negative impacts on people’s lives. Different public discussions happened during that situation on social media. It is essential to understand those discussions to prepare for the same kind of situation in future. Therefore, this study aims to identify the topics discussed on Twitter regarding this influence. During March 2020 and December 2021, 10,658 Tweets were gathered through the Twitter application programming interface and preprocessed using Python libraries. After feature extraction using the bag-of-words method, both probabilistic latent semantic analysis (PLSA) and latent Dirichlet allocation (LDA) were used as topic modeling methods. As a result of the analysis, 15 topics by LDA and 25 topics by PLSA were extracted during the study and then grouped into five key themes: Government responses for managing the COVID-19 Pandemic, Government decisions for COVID-19, Public response to government measures for COVID-19, Social influence, and Vaccination. Through a comparative evaluation of the LDA and PLSA topic modeling techniques, the research identifies LDA as the superior method, providing more accurate and coherent results.