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Textual emotion detection in health: Advances and applications
journal contribution
posted on 2023-02-12, 23:28 authored by AH Saffar, TK Mann, Bahadorreza OfoghiBahadorreza OfoghiTextual Emotion Detection (TED) is a rapidly growing area in Natural Language Processing (NLP) that aims to detect emotions expressed through text. In this paper, we provide a review of the latest research and development in TED as applied in health and medicine. We focus on medical and non-medical data types, use cases, and methods where TED has been integral in supporting decision-making. The application of NLP technologies in health, and particularly TED, requires high confidence that these technologies and technology-aided treatment will first, do no harm. Therefore, this review also aims to assess the accuracy of TED systems and provide an update on the state of the technology. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines were used in this review. With a specific focus on the identification of different human emotions in text, the more general sentiment analysis studies that only recognize the polarity of text were excluded. A total of 66 papers met the inclusion criteria. This review found that TED in health and medicine is mainly used in the detection of depression, suicidal ideation, and the mental status of patients with asthma, Alzheimer's disease, cancer, and diabetes with major data sources of social media, healthcare services, and counseling centers. Approximately, 44% of the research in the domain is related to COVID-19, investigating the public health response to vaccinations and the emotional response of the public. In most cases, deep learning-based NLP techniques were found to be preferred over other methods due to their superior performance. Developing methods for implementing and evaluating dimensional emotional models, resolving annotation challenges by utilizing health-related lexicons, and using deep learning techniques for multi-faceted and real-time applications were found to be among the main avenues for further development of TED applications in health.
History
Journal
Journal of Biomedical InformaticsVolume
137Article number
104258Pagination
104258-104258Location
United StatesPublisher DOI
ISSN
1532-0464eISSN
1532-0480Language
enPublisher
Elsevier BVUsage metrics
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No categories selectedKeywords
Health applicationsMachine learningMedical applicationsMental healthNatural language processingPhysical healthTextual emotion detectionBasic Behavioral and Social SciencePreventionBehavioral and Social ScienceMental HealthDepressionMind and Body3 Good Health and Well BeingBiological SciencesInformation and Computing SciencesMedical and Health Sciences
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