Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques
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 exi...
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| Language: | English |
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Elsevier
2025-06-01
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| Series: | Natural Language Processing Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S294971912500038X |
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| author | Juswin Sajan John Boppuru Rudra Prathap Gyanesh Gupta Jaivanth Melanaturu |
| author_facet | Juswin Sajan John Boppuru Rudra Prathap Gyanesh Gupta Jaivanth Melanaturu |
| author_sort | Juswin Sajan John |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3df2335fad2a45f3b7e1d1ceb760121c |
| institution | OA Journals |
| issn | 2949-7191 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Natural Language Processing Journal |
| spelling | doaj-art-3df2335fad2a45f3b7e1d1ceb760121c2025-08-20T02:35:48ZengElsevierNatural Language Processing Journal2949-71912025-06-011110016210.1016/j.nlp.2025.100162Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP TechniquesJuswin Sajan John0Boppuru Rudra Prathap1Gyanesh Gupta2Jaivanth Melanaturu3Department of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, IndiaDepartment of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru, India; Corresponding author.Department of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, IndiaDepartment of Computer Science and Engineering, CHRIST UNIVERSITY, Bengaluru, IndiaMental 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.http://www.sciencedirect.com/science/article/pii/S294971912500038XMental stressNatural language processingMachine learningDeep learningMental health researchLLM |
| spellingShingle | Juswin Sajan John Boppuru Rudra Prathap Gyanesh Gupta Jaivanth Melanaturu Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques Natural Language Processing Journal Mental stress Natural language processing Machine learning Deep learning Mental health research LLM |
| title | Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques |
| title_full | Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques |
| title_fullStr | Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques |
| title_full_unstemmed | Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques |
| title_short | Categorizing Mental Stress: A Consistency-Focused Benchmarking of ML and DL Models for Multi-Label, Multi-Class Classification via Taxonomy-Driven NLP Techniques |
| title_sort | categorizing mental stress a consistency focused benchmarking of ml and dl models for multi label multi class classification via taxonomy driven nlp techniques |
| topic | Mental stress Natural language processing Machine learning Deep learning Mental health research LLM |
| url | http://www.sciencedirect.com/science/article/pii/S294971912500038X |
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