ArticleBehavior research methods2021
How do you feel? Using natural language processing to automatically rate emotion in psychotherapy.
Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
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Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Large Language Models for Mental Health Applications: Systematic Review.JMIR mental health · 2024Pooled it
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Using natural language processing to track negative emotions in the daily lives of adolescents.Journal of psychopathology and clinical science · 2026Article
- Artificial intelligence in traumatic stress treatment: The TRUST framework for ethical development, clinical applications, and research advancement.Journal of traumatic stress · 2026Review
- Scoping review on natural language processing applications in counselling and psychotherapy.British journal of psychology (London, England : 1953) · 2026Article
- Systematic review and meta analysis of chatbots in the management of depressive and anxiety symptoms.NPJ digital medicine · 2026Article
- Domain-informed density extraction for robust mental health classification of long social media posts.Frontiers in artificial intelligence · 2026Article
- The association of transformer-based sentiment analysis with symptom distress and deterioration in routine psychotherapy care.Frontiers in digital health · 2026Article
- The role of prompt, voice, and personality factors in the acceptance and evaluation of AI-generated mindfulness exercises.Scientific reports · 2025Article
- Assessing Heterogeneity in Sentiment Changes in Text-Based Counseling: Latent Class Trajectory Analysis.Journal of medical Internet research · 2025Article
- An Examination of Generative AI Response to Suicide Inquires: Content Analysis.JMIR mental health · 2025Article
- Development and validation of large language model rating scales for automatically transcribed psychological therapy sessions.Scientific reports · 2025Article
- Advancing psychological assessment: quantifying self-compassion through free-text responses and language model BERT.Scientific reports · 2025Article
- Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis.Journal of medical Internet research · 2025Article
- The Applications of Large Language Models in Mental Health: Scoping Review.Journal of medical Internet research · 2025Article
- Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.Journal of medical Internet research · 2025Article
- Employing large language models for emotion detection in psychotherapy transcripts.Frontiers in psychiatry · 2025Article
- Evolution of Linguistic Markers of Agency, Centrality and Content During Metacognitive Therapy for Psychosis: A Pilot Exploratory Study.Early intervention in psychiatry · 2025Article
- Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing.NPJ digital medicine · 2024Article
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Authors and funding
9 authors.
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Abstract
Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist-client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.