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Tweets Classification of Mental Health Disorder in Indonesia Using LDA and Cosine Similarity
Abstract
Purpose: Twitter related to mental health has great potential as a medium to provide important information to the public and health organizations on a large scale, but an evaluation of tweet data related to mental health disorders has not been carried out. This study aims to classify tweet data to determine the most common mental health disorders in Indonesia based on the symptoms experienced.
Methodology: The classification process is carried out using cosine similarity calculations between tweets data and keywords which are compiled based on theoretical studies and optimization of the LDA topic modeling results.
Findings/result:The classification results show that the most discussed issues on Twitter are depression, bipolar, schizophrenia, dementia, and PTSD. Based on these results it can be interpreted that the level of prevalence and public attention to depressive diorders is quite high compared to other disorders. From the results of the classification, it is also possible to identify the most discussed symptoms throughthe emergence of keywords from each category.
Originality: Classification is calculated based on the cosine similarity between tweets and keywords compiled from human judgement and enriched using the results of LDA topic modeling to improve classification performanceKeywords
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DOI: https://doi.org/10.31315/telematika.v21i1.10725
DOI (PDF): https://doi.org/10.31315/telematika.v21i1.10725.g6343
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Status Kunjungan Jurnal Telematika