[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121231-en":3,"doc-seo-121231-105":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":4,"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},121231,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing","People’s mental states often become visible through anonymous social media activity, enabling earlier detection of psychiatric concerns before they escalate into conditions such as depression and anxiety. The work applies machine learning classifiers to label Reddit posts as stressful or non-stressful, using multiple NLP embedding approaches. ELMo word embeddings, BERT tokenizers, and Bag of Words representations are transformed into model-ready text features, and results across methods are evaluated. The best configuration reaches a top F1 of 0.76, precision 0.71, and recall 0.74.","Human-Centric Intelligent Systems (2023) 3:80–91 [https://doi.org/10.1007/s44230-023-00020-8](https://doi.org/10.1007/s44230-023-00020-8)  \nMachine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing  \nShaunak Inamdar1 · Rishikesh Chapekar1 · Shilpa Gite1,2 · Biswajeet Pradhan3  \nReceived: 8 September 2022 / Accepted: 14 March 2023 / Published online: 30 March 2023 © The Author(s) 2023  \nAbstract  \nPeople’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.  \nKeywords Stress analysis · ELMo embeddings · Machine learning · Natural language processing · BERT · TF-IDF  \n* Shilpa Gite [shilpa.gite@sitpune.edu.in](shilpa.gite@sitpune.edu.in)  \n* Biswajeet Pradhan [biswajeet.pradhan@uts.edu.au](biswajeet.pradhan@uts.edu.au)  \nShaunak Inamdar  \n[shaunak.inamdar.btech2019@sitpune.edu.in](shaunak.inamdar.btech2019@sitpune.edu.in)  \nRishikesh Chapekar  \n[chapekarrishikesh@gmail.com](chapekarrishikesh@gmail.com)  \n1 AIML Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India  \n2 Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India  \n3 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia  \n1 Introduction  \nThe field of stress analysis and sentiment analysis of postson microblogging sites has been blossoming in recent years. Mental stress leads to many health problems, and it is crucial to identify such cases and provide support. It is observed that high exposure to mental stress results in negative behavioral changes, both mental and physical [1] . In the past, stress analysis has been explored on data from microblogging sites like Twitter and Facebook. However, anonymized sites with longer-form content help give users more freedom in expressing their thoughts and underlying stress. This research provides insight into the topics and characteristics that stress people from all over the world by conducting stress analysis on Reddit posts and identifying indications of stress. This could be used as an opportunity to transform the mental health conversation and help early intervention for depression. There is a clear need for systems for early detection of stress and mental disorders.  \nThis paper considers posts from various subreddits on the popular social media platform Reddit. The dataset [2]  \ncontains about 2800 unique te","cbCaigns28D99gL8","https://ap.wps.com/l/cbCaigns28D99gL8","pdf",1031135,1,12,"English","en",105,"# Introduction\n## Problem background and motivation\n## Dataset and scope\n## Contributions and paper highlights\n## NLP and modeling approach","[{\"question\":\"How does the method detect mental stress in this study?\",\"answer\":\"It trains machine learning models to classify Reddit posts as stressful or non-stressful using text features generated by NLP embedding techniques.\"},{\"question\":\"Which NLP techniques and representations are used?\",\"answer\":\"The study uses ELMo embeddings, BERT tokenization, and Bag of Words/TF-IDF-style word/sentence representations to build inputs for classifiers.\"},{\"question\":\"What performance does the best model achieve?\",\"answer\":\"Using preprocessed texts with machine learning algorithms, the best result reports F1=0.76, precision=0.71, and recall=0.74.\"}]","Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing | PDF",1785734462,30,{"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-driven-mental-stress-detection-on-reddit-posts-using-natural-language-processing","",{"@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-driven-mental-stress-detection-on-reddit-posts-using-natural-language-processing/121231/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method detect mental stress in this study?","Question",{"text":75,"@type":76},"It trains machine learning models to classify Reddit posts as stressful or non-stressful using text features generated by NLP embedding techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which NLP techniques and representations are used?",{"text":80,"@type":76},"The study uses ELMo embeddings, BERT tokenization, and Bag of Words/TF-IDF-style word/sentence representations to build inputs for classifiers.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance does the best model achieve?",{"text":84,"@type":76},"Using preprocessed texts with machine learning algorithms, the best result reports F1=0.76, precision=0.71, and recall=0.74.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]