Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rajak, Akash; Mishra, Siddheshwari Dutt; Vidushi; Bhardwaj, Shashank; Kumar, Amit; Kumar, Sunil Title A name given to the resource AI that Understands Us: LLM-Based Emotion and Stress Insights from Online Communication Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Conference on Innovative Practices in Technology and Management, ICIPTM 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICIPTM69057.2026.11465340" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICIPTM69057.2026.11465340</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037801381?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037801381?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Rajak A., Krishna Institute of Engineering &amp; Technology (KIET), Uttar Pradesh, Ghaziabad, India; Mishra S.D., School of Computer Science and Engineering, Galgotias University, Greater Noida, India; Vidushi, School of Sciences, Christ (Deemed To Be University), Karnataka, Bengaluru, India; Bhardwaj S., Krishna Institute of Engineering &amp; Technology (KIET), Uttar Pradesh, Ghaziabad, India; Kumar A., Krishna Institute of Engineering &amp; Technology (KIET), Uttar Pradesh, Ghaziabad, India; Kumar S., Krishna Institute of Engineering &amp; Technology (KIET), Uttar Pradesh, Ghaziabad, India Description An account of the resource The large language models (LLMs) show more accurate interpretation of human emotion and psychology from text which is available on the social media chats. This study analyses emotion recognition and stress detection capabilities of fine-tuned LLMs (GPT-2, FLAN-T5 and LLaMA-7B) using text data from social media and conversation logs. The models were tested for performance using the publicly available emotion language datasets, DailyDialog, EmotionX and GoEmotions. The models were evaluated for performance and efficiency using classification accuracy, macro-F1 score, and inference time. The results identify the performance spectrum of the models and the large models' enhanced ability to recognize detailed emotional states. These results offer real world applicability of LLM methodologies to stress detecting and decision-support automated systems in mental healthcare. 2026 IEEE. Subject The topic of the resource Classification; Emotion; Healthcare; LLM; Social Chat; Social Media; Stress Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-831954328-8; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online