Automatic Categorization of Mental Health Frame in Indonesian X (Twitter) Text using Classification and Topic Detection Techniques - Basuki, Setio; Indrabayu, Rizky; Effendy, Nico Ardia (Universitas Muhammadiyah Surakarta, 2024)
This paper aims to develop a machine learning model to detect mental health frames in Indonesian-language tweets on the X (Twitter) platform. This research is motivated by the gap in automatically detecting mental health frames, despite the importance of mental health issues in Indonesia. This paper addresses the problem by applying classification and topic detection methods across various mental health frames through multiple stages. First, this paper examines various mental health frames, resulting in 7 main labels: Awareness, Classification, Feelings and Problematization, Accessibility and Funding, Stigma, Service, Youth, and an additional label named Others. Second, it focuses on constructing a dataset of Indonesian tweets, totaling 29,068 data, by filtering tweets using the keywords