Implementation of LSA for Topic Modeling on Tweets with the Keyword ‘Kemenkeu’ - Khariroh, Shofiyatul; Alzami, Farrikh; Indrayani, Heni; Dewi, Ika Novita; Marjuni, Aris; Adriani, Mira Riezky; Subowo, Moh Hadi (Politeknik Ganesha Medan, 2025)
This research explores public discourse on financial policies by analyzing tweets mentioning the keyword 'Kemenkeu' (Ministry of Finance). Using Latent Semantic Analysis (LSA), the study examined 10,099 tweets to uncover key topics that reflect public sentiment toward the Ministry’s policies. Preprocessing steps, such as stopword removal and stemming with Sastrawi, were essential to ensure the effectiveness of the analysis. The results revealed three main topics: Finance and Budget, Salaries and Employee Welfare, and Excise and Customs Regulations. These insights provide a better understanding of public opinion on financial issues and highlight the importance of proper text preprocessing in topic modeling. This approach demonstrates how LSA can be used as a tool for analyzing large-scale social media data, offering valuable input for policymakers. Future research could expand on this by using more advanced models or larger datasets to gain deeper insights.