ArticleFrontiers in psychiatry2021
Detecting Presence of PTSD Using Sentiment Analysis From Text Data.
Article in Frontiers in psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Linguistic markers for identifying post-traumatic stress disorder and associated symptoms: a systematic literature review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- The use of artificial intelligence in psychotherapy: development of intelligent therapeutic systems.BMC psychology · 2025Trial
- Detecting risk for depression and anxiety symptoms among older bereaved women using text-based analysis in the Nurses' Health Study II.American journal of epidemiology · 2026Article
- Optimizing Symptom Surveys with Machine Learning to Predict PTSD One Year Post-trauma....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2026Article
- Privacy and Human-AI Relationships.Philosophy & technology · 2025Article
- Acute suicidal ideation in context: highlighting sentiment-based markers through the diary entries of a clinically depressed sample.BMC psychiatry · 2025Article
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.PloS one · 2025Article
- Sex differences in PTSD speech biomarkers assessed by virtual agent-induced conversations.Frontiers in psychology · 2025Article
- What Is the Attitude of Romanian Smallholders Towards a Ground Mole Infestation? A Study Using Topic Modelling and Sentiment Analysis on Social Media and Blog Discussions.Animals : an open access journal from MDPI · 2024Article
- The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis.NPJ digital medicine · 2024Review
- Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.Clinical practice and epidemiology in mental health : CP & EMH · 2024Review
- War, emotions, mental health, and artificial intelligence.Frontiers in psychology · 2024Review
- Transference and the psychological interplay in AI-enhanced mental healthcare.Frontiers in psychiatry · 2024Article
- Automatic screening for posttraumatic stress disorder in early adolescents following the Ya'an earthquake using text mining techniques.Frontiers in psychiatry · 2024Article
- Systematic review of machine learning in PTSD studies for automated diagnosis evaluation.Npj mental health research · 2023Review
- Sentiments analysis of fMRI using automatically generated stimuli labels under naturalistic paradigm.Scientific reports · 2023Article
- Machine learning methods to discriminate posttraumatic stress disorder: A protocol of systematic review and meta-analysis.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.
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Registered trials
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.