Detection of Mental Disorders through Text Mining on Social Media

• Item type: SIG Technical Reports(1)
• 公開日: 2023-06-22
• タイトル: Detection of Mental Disorders through Text Mining on Social Media
• タイトル / 言語: en
• タイトル: Detection of Mental Disorders through Text Mining on Social Media
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 数理モデル化と問題解決1
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Nagoya Institute of Technology
• 著者所属: Nagoya Institute of Technology
• 著者所属: Nagoya Institute of Technology
• 著者所属: Nagoya Institute of Technology
• 著者所属: Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者所属(英): en
• 著者所属(英): Nagoya Institute of Technology
• 著者名: Julien, Ghali; Kosuke, Shima; Koichi, Moriyama; Atsuko, Mutoh; Nobuhiro, Inuzuka
• 著者名(英): Julien, Ghali; Kosuke, Shima; Koichi, Moriyama; Atsuko, Mutoh; Nobuhiro, Inuzuka
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA12055912
• 書誌情報: 研究報告バイオ情報学（BIO）; 巻 2023-BIO-74, 号 3, p. 1-6, 発行日 2023-06-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8590
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会