{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T06:45:41Z","timestamp":1782456341848,"version":"3.54.5"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Social media platforms produce a constant stream of user-generated text, which holds promise as a source of useful information regarding population-level and individual mental health states. However, existing methods for sentiment analysis on social media texts process these texts as individual, static instances, failing to consider the underlying dynamics of emotional states, which are naturally evolving in nature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this paper, we introduce a novel framework for Temporal Sentiment Progression Analysis, which uses domain-specific transformer architectures to reconstruct the entire emotional evolution of a user over a given period of time. This framework captures important aspects of emotional evolution, including variability, volatility, and key emotional inflection points in a user\u2019s emotional progression. Our approach performs per-comment sentiment and thematic classification using transformer models, followed by post hoc statistical analysis to examine temporal patterns in user discussions. The framework was evaluated on a dataset of approximately 4,000 Reddit comments collected from eight mental health-related subreddits, with additional synthetic samples used only during training to address class imbalance. Performance evaluation was conducted exclusively on authentic Reddit comments and further validated using external Reddit-based mental health datasets and comparative baseline experiments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our experimental results on a large-scale social media dataset reveal different sentiment progression archetypes, which are strongly correlated with self-reported mental health concerns. Our proposed framework achieves a high classification accuracy of 92%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Our approach shifts from static sentiment analysis to dynamic sentiment progression analysis, which can help in understanding the evolution of emotional distress in a more nuanced manner and can be useful in the development of context-aware interventions for mitigating mental health concerns.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frai.2026.1810552","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:45:49Z","timestamp":1782452749000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Sentiment trajectory modeling in mental health discussions via transformer-based analysis"],"prefix":"10.3389","volume":"9","author":[{"given":"Shaina","family":"U","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamilarasi Kathirvel","family":"Murugan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshaya Poorna","family":"R","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Logeswari","family":"Govindaraj","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel","family":"Prince","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"120553","DOI":"10.1109\/ACCESS.2024.3406469","article-title":"Detection and prediction of future mental disorder from social media data using machine learning, ensemble learning, and large language models","volume":"12","author":"Abdullah","year":"2024","journal-title":"IEEE Access"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"451","DOI":"10.13053\/cys-28-2-4873","article-title":"Mental illness classification on social media texts using deep learning and transfer learning","volume":"28","author":"Arif","year":"2024","journal-title":"Computaci\u00f3n y Sistemas"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"101079","DOI":"10.37349\/eff.2025.101079","article-title":"Therapeutic effects of ketogenic diets on physiological and mental health","volume":"3","author":"Borrego-Ruiz","year":"2025","journal-title":"Exploration of Foods and Foodomics"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e0299059","DOI":"10.1371\/journal.pone.0299059","article-title":"Cohort profile: the social media, smartphone use and self-harm in young people (3S-YP) study\u2013a prospective, observational cohort study of young people in contact with mental health services","volume":"19","author":"Bye","year":"2024","journal-title":"PLoS One"},{"key":"ref5","article-title":"Machine learning approaches for mental illness detection on social media: a systematic review of biases and methodological challenges","author":"Cao","year":"2024","journal-title":"arXiv"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"104499","DOI":"10.1016\/j.compbiomed.2021.104499","article-title":"A textual-based featuring approach for depression detection using machine learning classifiers and social media texts","volume":"135","author":"Chiong","year":"2021","journal-title":"Comput. 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