@article{oai:ipsj.ixsq.nii.ac.jp:00232854,
 author = {Julien, Ghali and Kosuke, Shima and Koichi, Moriyama and Atsuko, Mutoh and Nobuhiro, Inuzuka and Julien, Ghali and Kosuke, Shima and Koichi, Moriyama and Atsuko, Mutoh and Nobuhiro, Inuzuka},
 issue = {1},
 journal = {情報処理学会論文誌数理モデル化と応用（TOM）},
 month = {Feb},
 note = {Mental disorders are a growing concern worldwide, early detection and accurate identification can lead to better treatment outcomes. In this paper, we explore the use of machine learning techniques to vectorize and identify mental disorders from textual data. We used a database of 700,000 Reddit posts published in six different subreddit associated with mental disorders: Anxiety, Borderline Personality Disorder, Bipolar, Depression, Mental Illness, and Schizophrenia. We used the TF-IDF vectorizer to represent the posts as numerical vectors and applied UMAP dimensionality reduction to visualize the data in two dimensions. The application of UMAP allowed us identify clusters and to use k-means clustering to identify groups of posts with similar content. We found that some posts, despite being posted in specific mental disorders, were presenting signs of depression or anxiety. Our results suggest that machine learning techniques can help identify clusters and relationships between mental disorders, potentially leading to improved diagnosis and treatment., Mental disorders are a growing concern worldwide, early detection and accurate identification can lead to better treatment outcomes. In this paper, we explore the use of machine learning techniques to vectorize and identify mental disorders from textual data. We used a database of 700,000 Reddit posts published in six different subreddit associated with mental disorders: Anxiety, Borderline Personality Disorder, Bipolar, Depression, Mental Illness, and Schizophrenia. We used the TF-IDF vectorizer to represent the posts as numerical vectors and applied UMAP dimensionality reduction to visualize the data in two dimensions. The application of UMAP allowed us identify clusters and to use k-means clustering to identify groups of posts with similar content. We found that some posts, despite being posted in specific mental disorders, were presenting signs of depression or anxiety. Our results suggest that machine learning techniques can help identify clusters and relationships between mental disorders, potentially leading to improved diagnosis and treatment.},
 pages = {29--35},
 title = {Identification of Mental Disorders through Text Mining on Social Media},
 volume = {17},
 year = {2024}
}