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 Article Faculty Publications -Articles 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 John, Juswin Sajan; Prathap, Boppuru Rudra; Gupta, Gyanesh; Melanaturu, Jaivanth Title A name given to the resource Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Natural Language Processing Journal;Volume;11;Issue;;Article No.;100162; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.nlp.2025.100162" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.nlp.2025.100162</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105022080973?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105022080973?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 John J.S., Department of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, India; Prathap B.R., Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, India; Gupta G., Department of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, India; Melanaturu J., Department of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, India Description An account of the resource Mental stress, a critical concern worldwide, necessitates precise and nuanced characterization. This study introduces a novel approach to effectively characterize mental stress through a multi-label, multi-class classification framework through natural language processing techniques. Building on existing literature, discussions with psychologists and other mental health practitioners, we developed a taxonomy of 27 distinctive markers spread across 4 label categories; aiming to create a preliminary screening tool leveraging textual data. The core objective is to identify the most suitable model for this complex task, encompassing comprehensive evaluation of various machine learning and deep learning algorithms. we experimented with support vector machines (SVM), random forest (RF) and long short-term memory (LSTM) algorithms incorporating various feature combinations involving Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Dirichlet Allocation (LDA). The best performer of this comparative study was further evaluated against an LLM. The potential of large language models (LLMs), including their language understanding and prediction capabilities, is another key focus. We explore how these models could augment and advance mental health research, offering new perspectives and insights into the characterization of mental stress. Our findings show that the top model, an LSTM with TF-IDF and LDA (class weights assigned) outperformed the PaLM model with a coefficient of variation as low as 0.87% across all labels. Despite the PaLM model's superior average performance, it exhibited higher variability among different labels. 2025 The Author(s) Subject The topic of the resource Deep learning; LLM; Machine learning; Mental health research; Mental stress; Natural language processing Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 29497191; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online