Abstract

The rapid development of digital technology has revolutionized online and social media analysis, making it an indispensable tool for educational institutions such as Universitas Islam Negeri (UIN) Mataram to comprehend public perception and trending topics. By applying the Latent Dirichlet Allocation (LDA) approach to identify trending topics and the Lexicon-based method to analyze sentiment toward these topics, this research addresses the challenge of identifying key issues and public sentiment. Research data was collected from various news platforms and social media such as Twitter or X, Instagram, and Facebook, using the Google News API and Selenium for web scraping. The collected data was then processed to generate findings relevant to this research. The results of the LDA show that the most frequently discussed topics are related to the achievement of superior accreditation by UIN Mataram, student achievements, and academic activities. Meanwhile, using the lexicon-based method, the sentiment analysis results found that most topics related to UIN Mataram received dominant positive sentiment. Several topics showed positive sentiments, reaching almost 100%, such as topics 2 and 10, which were related to achieving accreditation. Meanwhile, several other issues were dominated by neutral sentiment, indicating that discussions on the topic tended to be informative without triggering significant emotions. Overall, the UIN Mataram institution was viewed positively in the news and media discussions analyzed.

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