[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119752-en":3,"doc-seo-119752-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119752,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning Approaches for Automated Mental Disorder Classification based on Social Media Textual Data","Machine learning models applied to mental-health text data provide an approach to capture patterns that support subgroup discovery and more personalized treatment options. The research builds a disorder classifier using text extracted from mental-health subreddits, forming a dataset of 10,000 text rows from BPD, bipolar, depression, anxiety, plus an others group. After text cleaning and TF-IDF vectorization, three models—Multinomial Naive Bayes, multilayer perceptron, and LightGBM—are trained and evaluated on both post titles and bodies. LightGBM achieves the highest accuracy, reaching 0.724 on titles and 0.77 on text content.","Research Article: Contemporary Issues in Behavioral and Social Sciences  \nMachine Learning Approaches for Automated Mental Disorder Classification based on Social Media Textual Data  \nKannan Nova  \nGrand Canyon University  \nThis work is licensed under a Creative Commons International License.  \nAbstract  \nThe application of machine learning models to mental health-related text data offers a novel approach to discern patterns and trends, aiding in the identification of subgroups and personalized treatment options. This research explores the classification of mental disorders based on text data extracted from subreddits focused on mental health. The dataset consists of 10,000 rows of text collected from four subreddits: 'BPD', 'bipolar', 'depression', and 'Anxiety', along with a combined category 'others' encompassing 'mentalillness' and 'schizophrenia'. To enable the application of machine learning models, various text preprocessing techniques were applied, including the removal of URLs, punctuation marks, and stopwords, as well as the transformation of raw text documents into a matrix ofTF-IDF features. These preprocessing steps were performed on both the titles and text contents of the posts. Three machine learning models, namely Multinomial Naive Bayes, Multilayer Perceptron, and LightGBM, were employed for the classification task. The models were trained and evaluated separately on both the post titles and the text content. The accuracy of each model was assessed to measure their performance. The results indicate that the Multinomial Naive Bayes model achieved an accuracy of 0.706 when classifying based on titles, while the accuracy increased to 0.73 when classifying based on the text content. The Multi-layer Perceptron model yielded an accuracy of 0.68 for title classification and 0.714 for text content classification. Notably, the LightGBM model exhibited superior performance, achieving an accuracy of 0.724 when using titles for classification, and an even higher accuracy of 0.77 when employing the text content. This research demonstrates the efficacy of machine learning models in classifying mental disorders using text data extracted from social media. These findings contribute to the ongoing exploration of using social media data for mental health analysis and may aid in developing automated tools for early detection and support for individuals facing mental health challenges.  \nKeywords: Machine learning, Mental disorders, Text data, Reddit, NLP, BPD, Bipolar  \nIntroduction  \nThe advent of machine learning models has transformed the landscape of mental health by providing robust instruments to analyze and comprehend textual information [1] . These models can be trained to process vast amounts of textual information, such as online forums, social media posts, or clinical notes, and extract valuable insights that can aid in mental health research, diagnosis, and treatment. By employing techniques like natural language processing (NLP) and sentiment analysis, machine learning models can uncover patterns, sentiments, and themes within mental health-related text data [2], [3] .  \nOne significant application of machine learning models in mental health is the identification of individuals at risk of mental health disorders. By analyzing text data from sources like social  \nmedia posts or online support forums, these models can detect signs of distress, emotional instability, or other indicators of mental health conditions. This information can help mental health professionals and researchers identify high-risk individuals who may require immediate intervention or support [4] . Moreover, machine learning models can also aid in early detection by flagging subtle linguistic cues or changes in language patterns that might indicate the onset of mental health issues.  \nMachine learning models can also contribute to improving mental health treatment and therapy by analyzing text data from therapy sessions or self-reporting tools. Th","cbCaia6Yp9xhLiKo","https://ap.wps.com/l/cbCaia6Yp9xhLiKo","pdf",993734,1,14,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Background and rationale\n## Applications in risk detection and early intervention\n## Applications in treatment improvement and monitoring\n## Reducing stigma through automated analysis\n## Context of the COVID-19 pandemic","[{\"question\":\"How is the dataset for mental disorder classification constructed?\",\"answer\":\"Text data is collected from four mental-health subreddits (BPD, bipolar, depression, and Anxiety) and an additional combined category “others” containing mentalillness and schizophrenia, totaling 10,000 rows.\"},{\"question\":\"What preprocessing and feature representation are used before modeling?\",\"answer\":\"The study removes URLs, punctuation, and stopwords, then converts the text into TF-IDF feature matrices for both post titles and post text.\"},{\"question\":\"Which machine learning model performs best, and what accuracies are reported?\",\"answer\":\"LightGBM performs best, achieving 0.724 accuracy with titles and 0.77 accuracy with text content. Other models include Multinomial Naive Bayes and multilayer perceptron with lower results.\"}]","Machine Learning Approaches for Automated Mental Disorder Classification based on Social Media Textual Data | PDF",1785726124,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-approaches-for-automated-mental-disorder-classification-based-on-social-media-textual-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-approaches-for-automated-mental-disorder-classification-based-on-social-media-textual-data/119752/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is the dataset for mental disorder classification constructed?","Question",{"text":75,"@type":76},"Text data is collected from four mental-health subreddits (BPD, bipolar, depression, and Anxiety) and an additional combined category “others” containing mentalillness and schizophrenia, totaling 10,000 rows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What preprocessing and feature representation are used before modeling?",{"text":80,"@type":76},"The study removes URLs, punctuation, and stopwords, then converts the text into TF-IDF feature matrices for both post titles and post text.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best, and what accuracies are reported?",{"text":84,"@type":76},"LightGBM performs best, achieving 0.724 accuracy with titles and 0.77 accuracy with text content. 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