Pendekatan Hibrid Berbasis NLP untuk Deteksi Emosi dan Kesehatan Mental di X.com dengan TF-IDF dan BERTopic yang Dioptimasi - Ahmad, Abdullah; Safii, M.; Andani, Sundari Retno; Wanto, Anjar (Seminar Nasional Informatika (SENATIKA), 2026)
This study was conducted to analyze and compare the effectiveness of two natural language processing-based topic modeling approaches in detecting emotions and mental health in text from the X.com platform. The baseline model was built using the Term Frequency–Inverse Document Frequency representation integrated with the BERTopic algorithm. Next, parameter optimization was performed on the dimensionality reduction and clustering components to improve topic coherence. Test results showed that the optimized model resulted in an increase in coherence value from 0.327 to 0.398, indicating stronger and more consistent semantic relationships between words within a topic. Based on the analysis, the context-based approach proved superior in generating representative and relevant topics related to emotional expressions and mental health issues on social media.