Pemodelan Topik pada Komunitas Ekspresi Emosi Negatif di Media Sosial X Menggunakan LDA - Rizal Muhammad Ramli; Chanifah Indah Ratnasari (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
This study aims to map the thematic structure of conversations within a community of negative emotional expression on platform X, commonly referred to as the “Komunitas MARAH MARAH.” The primary problem explored in this study is how collective anger is formed and which issues dominate the discourse within this community. To address this, the study employs a text mining approach through several stages of textual data processing, including data scraping, preprocessing, dictionary-based normalization, Term Frequency–Inverse Document Frequency (TF-IDF) weighting, and topic modeling using Latent Dirichlet Allocation (LDA). A total of 75,032 tweets were collected and subsequently cleaned, resulting in 38,956 unique entries for further analysis. Topic modeling was conducted by evaluating several topic configurations, with the highest coherence score of 0.5367 achieved using a three-topic model. Further analysis revealed three dominant themes along with their proportional distributions: personal complaints and everyday emotional expression (50.5%), direct anger or generalized expressions toward particular groups (35.8%), and issues related to fraud and digital security (13.7%). These findings illustrate how collective anger is constructed, disseminated, and interpreted within online conversational spaces. This study is expected to serve as a foundation for further research on digital emotion, online community dynamics, and social issue mapping through public discourse.