{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:45:14Z","timestamp":1792172714307,"version":"4.4.0"},"reference-count":83,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Personality traits are central to understanding human behavior and life outcomes, yet their assessment remains challenging due to biases and limited expressiveness in traditional self-report inventories. The emergence of large language models (LLMs) creates new opportunities to infer personality from naturalistic text, but it remains unclear whether these systems can reliably predict validated personality traits or whether their predictions primarily reflect superficial linguistic patterns.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study evaluates the ability of LLM-based methods to predict continuous Big Five personality scores (1\u20105 Likert scale) from semistructured interview transcripts and compares the performance of different modeling paradigms.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We analyzed semistructured interview transcripts from 518 participants, each paired with validated self-reported Big Five scores measured using the 10-item Big Five Inventory (BFI-10). We compared four paradigms: (1) zero-shot and chain-of-thought prompting with GPT-5 mini; (2) parameter-efficient fine-tuning (low-rank adaptation) applied to open-source RoBERTa-base and LLaMA-2-7B models; (3) supervised fine-tuning of GPT-4.1 mini across two independent training runs; and (4) embedding-based regression using 4 pretrained encoders (all-mpnet-base-v2, E5-large-v2, text-embedding-3-small, and text-embedding-3-large) with a multioutput ridge regression.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Supervised fine-tuning yielded the strongest overall performance among the evaluated approaches. In the best-performing run, correlations reached\n r<\/jats:italic>\n =0.718 for conscientiousness and\n r<\/jats:italic>\n =0.631 for agreeableness. Neuroticism showed moderate and consistent performance across the 2 fine-tuning runs, whereas extraversion was less stable across runs. Embedding-based regression, particularly with text-embedding-3-large, achieved competitive results for conscientiousness and agreeableness, although it remained below supervised fine-tuning. Prompt-based methods showed weak and inconsistent performance across traits. Openness was generally difficult to predict, although RoBERTa-base with embedding-based low-rank adaptation fine-tuning showed modest but statistically significant performance, indicating trait- and method-dependent variability.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n LLM-based approaches exhibit trait-dependent performance in predicting personality from naturalistic dialogue, with fine-tuning substantially improving correspondence with validated psychometric scores. These findings suggest that personality inference from naturalistic dialogue is feasible but depends strongly on modeling strategy, trait domain, and the availability of supervised fine-tuning with validated personality measures.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/87129","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T14:55:09Z","timestamp":1787669709000},"page":"e87129-e87129","source":"Crossref","is-referenced-by-count":0,"title":["Personality Inference From Semistructured Interviews Using Large Language Models: Comparative Evaluation Study"],"prefix":"10.2196","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4779-3696","authenticated-orcid":false,"given":"Jianfeng","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3390-0259","authenticated-orcid":false,"given":"Julina","family":"Maharjan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3341-7925","authenticated-orcid":false,"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2372-0081","authenticated-orcid":false,"given":"Karin G","family":"Coifman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-4243","authenticated-orcid":false,"given":"Ruoming","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"issue":"6","key":"R1","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1037\/pspp0000472","article-title":"Do changes in personality predict life outcomes?","volume":"125","author":"Wright","journal-title":"J Pers Soc Psychol"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1111\/j.1745-6916.2007.00047.x","article-title":"The power of personality: the comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes","volume":"2","author":"Roberts","journal-title":"Perspect Psychol Sci"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1037\/rev0000399","article-title":"Cognitive and personality predictors of school performance from preschool to secondary school: an overarching model","volume":"130","author":"Demetriou","journal-title":"Psychol Rev"},{"key":"R4","doi-asserted-by":"crossref","unstructured":"Akrami N Fernquist J Isbister T Kaati L Pelzer B . Automatic extraction of personality from text: challenges and opportunities. Presented at: 2019 IEEE International Conference on Big Data (Big Data); Dec 9-12, 2019; Los Angeles, CA, USA. [doi: 10.1109\/BigData47090.2019.9005467]","DOI":"10.1109\/BigData47090.2019.9005467"},{"key":"R5","unstructured":"McDonald J . Measuring personality constructs: the advantages and disadvantages of self-reports, informant reports and behavioural assessments. University of Nottingham; 2008. URL: https:\/\/www.nottingham.ac.uk\/sociology\/documents\/enquire\/volume-1-issue-1-dodorico-mcdonald.pdf [Accessed 03-10-2026]"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.887616","article-title":"Linguistic measures of personality in group discussions","volume":"13","author":"Spitzley","journal-title":"Front Psychol"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-psych-120710-100419","article-title":"Personality processes: mechanisms by which personality traits \u201cget outside the skin\u201d","volume":"63","author":"Hampson","journal-title":"Annu Rev Psychol"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrp.2024.104528","article-title":"How to measure and model personality traits in everyday life: a qualitative analysis of 300 big five personality items","volume":"112","author":"Andresen","journal-title":"J Res Pers"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3261339","article-title":"Words similarities on personalities: a language-based generalization approach for personality factors recognition","volume":"11","author":"Santos","journal-title":"IEEE Access"},{"issue":"1","key":"R10","doi-asserted-by":"publisher","DOI":"10.3390\/bs16010135","article-title":"Psychometrics of drawmetrics: an expressive\u2013semantic framework for personality assessment","volume":"16","author":"Price","journal-title":"Behav Sci (Basel)"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.1016\/j.paid.2023.112514","article-title":"Modeling personality language use with small semantic vector subspaces","volume":"219","author":"Mart\u00ednez-Huertas","journal-title":"Pers Individ Dif"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1177\/25152459251343582","article-title":"Large language models for psychological assessment: a comprehensive overview","volume":"8","author":"Brickman","journal-title":"Adv Methods Pract Psychol Sci"},{"key":"R13","unstructured":"Jiang G Xu M Zhu SC Han W Zhang C Zhu Y . Evaluating and inducing personality in pre-trained language models. Presented at: NIPS \u201923: Proceedings of the 37th International Conference on Neural Information Processing Systems; Dec 10-16, 2023; New Orleans, LA, USA. [doi: 10.5555\/3666122.3666588]"},{"issue":"8","key":"R14","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s10462-025-11245-3","article-title":"Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges","volume":"58","author":"Naz","journal-title":"Artif Intell Rev"},{"issue":"12","key":"R15","doi-asserted-by":"publisher","first-page":"1954","DOI":"10.1038\/s42256-025-01115-6","article-title":"A psychometric framework for evaluating and shaping personality traits in large language models","volume":"7","author":"Serapio-Garc\u00eda","journal-title":"Nat Mach Intell"},{"key":"R16","unstructured":"Mat\u0161enas A Lello A Lees T Peep H Tamm KL . Can LLMs assess personality? Validating conversational AI for trait profiling. arXiv. Preprint posted online on Jan 23, 2026. [doi: 10.48550\/arXiv.2602.15848]"},{"issue":"4","key":"R17","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1111\/jopy.70019","article-title":"Inferring personality from social media activity using large language models: cross-model agreement, temporal stability, and convergent validity with self-reports","volume":"94","author":"Marengo","journal-title":"J Pers"},{"issue":"1","key":"R18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-53335-2","article-title":"Attitudes towards AI: measurement and associations with personality","volume":"14","author":"Stein","journal-title":"Sci Rep"},{"key":"R19","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1484260","article-title":"On the emergent capabilities of ChatGPT 4 to estimate personality traits","volume":"8","author":"Piastra","journal-title":"Front Artif Intell"},{"key":"R20","unstructured":"Liu YF Lu YL He D Zhang H . From five dimensions to many: large language models as precise and interpretable psychological profilers. Presented at: The Fourteenth International Conference on Learning Representations (ICLR 2026); Apr 23-27, 2026; Rio de Janeiro, Brazil. URL: https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2026\/file\/3d312e0c52a714b3c340507cd6bbf3e1-Paper-Conference.pdf [Accessed 03-09-2026]"},{"key":"R21","doi-asserted-by":"crossref","unstructured":"Mehta Y Fatehi S Kazameini A Stachl C Cambria E Eetemadi S . Bottom-up and top-down: predicting personality with psycholinguistic and language model features. Presented at: 2020 IEEE International Conference on Data Mining (ICDM); Nov 17-20, 2020; Sorrento, Italy. [doi: 10.1109\/ICDM50108.2020.00146]","DOI":"10.1109\/ICDM50108.2020.00146"},{"key":"R22","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.865841","article-title":"Explainable personality prediction using answers to open-ended interview questions","volume":"13","author":"Dai","journal-title":"Front Psychol"},{"issue":"6","key":"R23","doi-asserted-by":"publisher","DOI":"10.20900\/jpbs.20250020","article-title":"Benchmarking personality inference in large language models using real-world conversations","volume":"10","author":"Zhu","journal-title":"J Psychiatry Brain Sci"},{"key":"R24","unstructured":"Zhu J Jin R Coifman KG . Can LLMs infer personality from real-world conversations? arXiv. Preprint posted online on Jul 18, 2025. [doi: 10.48550\/arXiv.2507.14355]"},{"key":"R25","doi-asserted-by":"publisher","DOI":"10.2196\/75347","article-title":"Psychometric evaluation of large language model embeddings for personality trait prediction","volume":"27","author":"Maharjan","journal-title":"J Med Internet Res"},{"issue":"3","key":"R26","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.jrp.2010.04.001","article-title":"Personality in 100,000 words: a large-scale analysis of personality and word use among bloggers","volume":"44","author":"Yarkoni","journal-title":"J Res Pers"},{"key":"R27","doi-asserted-by":"crossref","unstructured":"Dagha R Dhaord R Lakhani V Hirlekar V . Personality prediction based on users\u2019 tweets. Presented at: 2022 2nd International Conference on Intelligent Technologies (CONIT); Jun 24-26, 2022; Hubli, India. [doi: 10.1109\/CONIT55038.2022.9847670]","DOI":"10.1109\/CONIT55038.2022.9847670"},{"issue":"3","key":"R28","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1111\/jopy.12674","article-title":"Regional personality assessment through social media language","volume":"90","author":"Giorgi","journal-title":"J Pers"},{"issue":"6","key":"R29","doi-asserted-by":"publisher","DOI":"10.1093\/pnasnexus\/pgae231","article-title":"Large language models can infer psychological dispositions of social media users","volume":"3","author":"Peters","journal-title":"PNAS Nexus"},{"issue":"3","key":"R30","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1038\/s41562-025-02389-x","article-title":"Assessing personality using zero-shot generative AI scoring of brief open-ended text","volume":"10","author":"Wright","journal-title":"Nat Hum Behav"},{"key":"R31","unstructured":"John OP Srivastava S . The Big Five trait taxonomy: history, measurement, and theoretical perspectives. In: Pervin LA John OP , editors. Handbook of Personality: Theory and Research. 2nd ed. Guilford Press; 1999:102-138. URL: https:\/\/jenni.uchicago.edu\/econ-psych-traits\/John_Srivastava_1995_big5.pdf [Accessed 03-10-2026]"},{"key":"R32","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107852","article-title":"Personality prediction model for social media using machine learning technique","volume":"100","author":"Kamalesh","journal-title":"Comput Electr Eng"},{"key":"R33","unstructured":"Boyd RL Ashokkumar A Seraj S Pennebaker JW . The development and psychometric properties of LIWC-22. University of Texas at Austin; 2022. URL: https:\/\/www.liwc.app\/static\/documents\/LIWC-22%20Manual%20-%20Development%20and%20Psychometrics.pdf [Accessed 03-10-2026]"},{"key":"R34","doi-asserted-by":"crossref","unstructured":"Vishnubhotla K Mohammad SM . Tweet emotion dynamics: emotion word usage in tweets from US and Canada. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association; 2022:4162-4176. URL: https:\/\/aclanthology.org\/2022.lrec-1.442\/ [Accessed 03-10-2026]","DOI":"10.63317\/3uekmcxqrbyb"},{"issue":"C","key":"R35","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.eswa.2016.09.009","article-title":"Turning from TF-IDF to TF-IGM for term weighting in text classification","volume":"66","author":"Chen","journal-title":"Expert Syst Appl"},{"issue":"1","key":"R36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-53989-y","article-title":"Personality prediction from task-oriented and open-domain human-machine dialogues","volume":"14","author":"Guo","journal-title":"Sci Rep"},{"issue":"4","key":"R37","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1111\/ajsp.12531","article-title":"Social media addiction and personality: a meta\u2010analysis","volume":"25","author":"Huang","journal-title":"Asian J Social Psychol"},{"issue":"6","key":"R38","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1037\/bul0000430","article-title":"Digital data and personality: a systematic review and meta-analysis of human perception and computer prediction","volume":"150","author":"Hinds","journal-title":"Psychol Bull"},{"key":"R39","unstructured":"Liu Y Ott M Goyal N et al. RoBERTa: a robustly optimized BERT pretraining approach. arXiv. Preprint posted online on Jul 26, 2019. [doi: 10.48550\/arXiv.1907.11692]"},{"key":"R40","unstructured":"Devlin J Chang MW Lee K Toutanova K . BERT: pre-training of deep bidirectional transformers for language understanding. arXiv. Preprint posted online on Oct 11, 2018. [doi: 10.48550\/arXiv.1810.04805]"},{"issue":"15","key":"R41","doi-asserted-by":"publisher","first-page":"5802","DOI":"10.1073\/pnas.1218772110","article-title":"Private traits and attributes are predictable from digital records of human behavior","volume":"110","author":"Kosinski","journal-title":"Proc Natl Acad Sci U S A"},{"key":"R42","doi-asserted-by":"crossref","unstructured":"Gjurkovi\u0107 M Karan M Vukojevi\u0107 I Bo\u0161njak M Snajder J . PANDORA talks: personality and demographics on Reddit. In: Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media. Association for Computational Linguistics; 2021:138-152. [doi: 10.18653\/v1\/2021.socialnlp-1.12]","DOI":"10.18653\/v1\/2021.socialnlp-1.12"},{"issue":"9","key":"R43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0073791","article-title":"Personality, gender, and age in the language of social media: the open-vocabulary approach","volume":"8","author":"Schwartz","journal-title":"PLoS One"},{"key":"R44","unstructured":"Datta A Chakraborty S Mukherjee A . Personality detection and analysis using Twitter data. arXiv. Preprint posted online on Sep 11, 2023. [doi: 10.48550\/arXiv.2309.05497]"},{"key":"R45","doi-asserted-by":"crossref","unstructured":"Zhang S Dinan E Urbanek J Szlam A Kiela D Weston J . Personalizing dialogue agents: I have a dog, do you have pets too? In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics; 2018:2204-2213. [doi: 10.18653\/v1\/P18-1205]","DOI":"10.18653\/v1\/P18-1205"},{"key":"R46","doi-asserted-by":"crossref","unstructured":"Lal YK Nilsson A Schwartz H . Systematic evaluation of GPT-3 for zero-shot personality estimation. In: Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis. Association for Computational Linguistics; 2023:390-400. [doi: 10.18653\/v1\/2023.wassa-1.34]","DOI":"10.18653\/v1\/2023.wassa-1.34"},{"key":"R47","unstructured":"Ramirez A Alsalihy M Aggarwal K Li C Wu L Walker M . Controlling personality style in dialogue with zero-shot prompt-based learning. arXiv. Preprint posted online on Feb 8, 2023. [doi: 10.48550\/arXiv.2302.03848]"},{"issue":"34","key":"R48","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2308950121","article-title":"GPT is an effective tool for multilingual psychological text analysis","volume":"121","author":"Rathje","journal-title":"Proc Natl Acad Sci U S A"},{"key":"R49","doi-asserted-by":"crossref","unstructured":"Yang T Shi T Wan F et al. PsyCoT: psychological questionnaire as powerful chain-of-thought for personality detection. In: Bouamor H Pino J Bali K , editors. Findings of the Association for Computational Linguistics. Association for Computational Linguistics; 2023:3305-3320. [doi: 10.18653\/v1\/2023.findings-emnlp.216]","DOI":"10.18653\/v1\/2023.findings-emnlp.216"},{"key":"R50","unstructured":"Hu EJ Shen Y Wallis P et al. LoRA: low-rank adaptation of large language models. arXiv. Preprint posted online on Jun 17, 2021. [doi: 10.48550\/arXiv.2106.09685]"},{"key":"R51","doi-asserted-by":"crossref","unstructured":"Banerjee AM Mukhopadhyaya K . Fine-tuning large language models for personality development. Presented at: 2025 9th International Conference on Electronics, Communication and Aerospace Technology (ICECA); Nov 5-7, 2025; Coimbatore, India. [doi: 10.1109\/ICECA66444.2025.11382960]","DOI":"10.1109\/ICECA66444.2025.11382960"},{"key":"R52","doi-asserted-by":"crossref","unstructured":"Shen L Long Y Cai X Chen G Razzak I Jameel S . Less but better: parameter-efficient fine-tuning of large language models for personality detection. Presented at: 2025 International Joint Conference on Neural Networks (IJCNN); Jun 30 to Jul 5, 2025; Rome, Italy. [doi: 10.1109\/IJCNN64981.2025.11228339]","DOI":"10.1109\/IJCNN64981.2025.11228339"},{"key":"R53","unstructured":"Lewis P Perez E Piktus A et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv. Preprint posted online on May 22, 2020. [doi: 10.48550\/arXiv.2005.11401]"},{"key":"R54","unstructured":"Zerhoudi S Granitzer M . PersonaRAG: enhancing retrieval-augmented generation systems with user-centric agents. arXiv. Preprint posted online on Jul 12, 2024. [doi: 10.48550\/arXiv.2407.09394]"},{"issue":"1","key":"R55","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s40537-021-00459-1","article-title":"Text based personality prediction from multiple social media data sources using pre-trained language model and model averaging","volume":"8","author":"Christian","journal-title":"J Big Data"},{"key":"R56","unstructured":"Ji Y Wu W Zheng H Hu Y Chen X He L . Is ChatGPT a good personality recognizer? A preliminary study. arXiv. Preprint posted online on Jul 8, 2023. [doi: 10.48550\/arXiv.2307.03952]"},{"issue":"28","key":"R57","doi-asserted-by":"publisher","first-page":"20619","DOI":"10.1007\/s00521-023-08846-w","article-title":"A deep learning approach to text-based personality prediction using multiple data sources mapping","volume":"35","author":"Sirasapalli","journal-title":"Neural Comput Applic"},{"key":"R58","doi-asserted-by":"crossref","unstructured":"Hu L He H Wang D Zhao Z Shao Y Nie L . LLM vs small model? Large language model based text augmentation enhanced personality detection model. Presented at: AAAI\u201924\/IAAI\u201924\/EAAI\u201924: Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence; Feb 20-27, 2024; Vancouver, British Columbia, Canada. [doi: 10.1609\/aaai.v38i16.29782]","DOI":"10.1609\/aaai.v38i16.29782"},{"issue":"1","key":"R59","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1038\/s41598-024-84109-5","article-title":"Evaluating the ability of large language models to emulate personality","volume":"15","author":"Wang","journal-title":"Sci Rep"},{"key":"R60","unstructured":"Coifman K . Pooled experiential and diagnostic interviews of at-risk adults. Open Science Framework (OSF). 2026. URL: https:\/\/osf.io\/357yk\/overview?view_only=12566422d5394a6eb78b89e6e597fe01 [Accessed 13-02-2026]"},{"key":"R61","unstructured":"LLM-personality-inference-interviews. GitHub. 2026. URL: https:\/\/github.com\/Sallyzhu\/LLM-Personality-Inference-Interviews [Accessed 03-10-2026]"},{"issue":"3","key":"R62","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1037\/a0020113","article-title":"When distress does not become depression: emotion context sensitivity and adjustment to bereavement","volume":"119","author":"Coifman","journal-title":"J Abnorm Psychol"},{"issue":"4","key":"R63","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1007\/s11031-016-9553-y","article-title":"When context matters: negative emotions predict psychological health and adjustment","volume":"40","author":"Coifman","journal-title":"Motiv Emot"},{"issue":"5","key":"R64","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1177\/1359105314532152","article-title":"Contextually appropriate emotional word use predicts adaptive health behavior: emotion context sensitivity and treatment adherence","volume":"21","author":"Harvey","journal-title":"J Health Psychol"},{"key":"R65","unstructured":"GPT-5 mini model. OpenAI API. URL: https:\/\/developers.openai.com\/api\/docs\/models\/gpt-5-mini [Accessed 10-05-2026]"},{"key":"R66","unstructured":"Kojima T Gu SS Reid M Matsuo Y Iwasawa Y . Large language models are zero-shot reasoners. Presented at: NIPS\u201922: Proceedings of the 36th International Conference on Neural Information Processing Systems; Dec 28-9, 2022; New Orleans, LA, USA. [doi: 10.5555\/3600270.3601883]"},{"key":"R67","unstructured":"Wei J Wang X Schuurmans D et al. Chain-of-thought prompting elicits reasoning in large language models. Presented at: NIPS\u201922: Proceedings of the 36th International Conference on Neural Information Processing Systems; Dec 28-9, 2022; New Orleans, Louisiana, USA. [doi: 10.5555\/3600270.3602070]"},{"key":"R68","unstructured":"Llama-2-7b-chat-hf. Hugging Face. 2024. URL: https:\/\/huggingface.co\/meta-llama\/Llama-2-7b-chat-hf [Accessed 19-04-2026]"},{"key":"R69","unstructured":"GPT-4.1 mini model. OpenAI Developers. URL: https:\/\/developers.openai.com\/api\/docs\/models\/gpt-4.1-mini [Accessed 09-04-2026]"},{"key":"R70","unstructured":"All-mpnet-base-v2. Hugging Face. 2024. URL: https:\/\/huggingface.co\/sentence-transformers\/all-mpnet-base-v2 [Accessed 16-04-2026]"},{"key":"R71","unstructured":"Wang L Yang N Huang X et al. Text embeddings by weakly-supervised contrastive pre-training. arXiv. Preprint posted online on Dec 7, 2022. [doi: 10.48550\/arXiv.2212.03533]"},{"key":"R72","unstructured":"Text-embedding-3-small model. OpenAI Developers. URL: https:\/\/developers.openai.com\/api\/docs\/models\/text-embedding-3-small [Accessed 16-04-2026]"},{"key":"R73","unstructured":"Ridge. scikit-learn. URL: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.Ridge.html [Accessed 16-04-2026]"},{"issue":"1","key":"R74","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1002\/jts.22992","article-title":"Recalled protective childhood experiences are associated with positive emotional expression in active\u2010duty firefighters","volume":"37","author":"Baugher","journal-title":"J Trauma Stress"},{"key":"R75","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2021.687497","article-title":"Feel to heal: negative emotion differentiation promotes medication adherence in multiple sclerosis","volume":"12","author":"Seah","journal-title":"Front Psychol"},{"key":"R76","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2022.1061621","article-title":"Were there losses in social support during the pandemic? Testing the impact of COVID-19 on psychological adjustment to trauma in United States adults","volume":"13","author":"Mitchell","journal-title":"Front Psychol"},{"issue":"5","key":"R77","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1080\/07448481.2021.1947297","article-title":"Personality or pathology? Predictors of early substance use in first-year college students","volume":"71","author":"Mitchell","journal-title":"J Am Coll Health"},{"issue":"3","key":"R78","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1007\/s10608-021-10284-8","article-title":"From social avoidance to substance use: working memory and negative affectivity predict maladaptive regulatory behaviors in daily life across diagnostic groups","volume":"46","author":"Coifman","journal-title":"Cogn Ther Res"},{"issue":"1","key":"R79","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1037\/abn0001047","article-title":"Language-based AI modeling of personality traits and pathology from life narrative interviews","volume":"135","author":"Oltmanns","journal-title":"J Psychopathol Clin Sci"},{"key":"R80","unstructured":"Lyu Y Zhang L . Ask, answer, and detect: role-playing LLMs for personality detection with question-conditioned mixture-of-experts. arXiv. Preprint posted online on Dec 9, 2025. [doi: 10.48550\/arXiv.2512.08814]"},{"issue":"1","key":"R81","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-81047-0","article-title":"Speech-based personality prediction using deep learning with acoustic and linguistic embeddings","volume":"14","author":"Lukac","journal-title":"Sci Rep"},{"key":"R82","unstructured":"Samadi MA Nixon N . Personalities at play: probing alignment in AI teammates. arXiv. Preprint posted online on Feb 28, 2026. [doi: 10.48550\/arXiv.2603.00429]"},{"key":"R83","unstructured":"Chhoun S Po P Ros S Cho WS Khoeurn S . Evaluation of chunking strategies for effective text embedding in low-resource language on agricultural documents. arXiv. Preprint posted online on May 21, 2026. [doi: 10.48550\/arXiv.2605.22203]"}],"container-title":["Journal of Medical Internet Research"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:53:21Z","timestamp":1792169601000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jmir.org\/2026\/1\/e87129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,16]]},"references-count":83,"URL":"https:\/\/doi.org\/10.2196\/87129","relation":{},"ISSN":["1438-8871"],"issn-type":[{"value":"1438-8871","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,16]]},"article-number":"v28i6e87129"}}