@techreport{oai:ipsj.ixsq.nii.ac.jp:00226471,
 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 = {3},
 month = {Jun},
 note = {Mental disorders are a growing concern worldwide, early detection and accurate identification can lead to better treatment outcomes. In this technical review, 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. We then applied k-means clustering to identify groups of posts with similar content. We found that some posts related to anxiety and mental illness were clustered together with schizophrenia posts, indicating similar symptoms and themes. Our results suggest that machine learning techniques can help identify patterns 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 technical review, 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. We then applied k-means clustering to identify groups of posts with similar content. We found that some posts related to anxiety and mental illness were clustered together with schizophrenia posts, indicating similar symptoms and themes. Our results suggest that machine learning techniques can help identify patterns and relationships between mental disorders, potentially leading to improved diagnosis and treatment.},
 title = {Detection of Mental Disorders through Text Mining on Social Media},
 year = {2023}
}