ArticleInternational journal of environmental research and public health2019
Psychological Disorder Identifying Method Based on Emotion Perception over Social Networks.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Effect analysis of repeated transcranial magnetic stimulation combined with fluoxetine in the treatment of first-episode adolescent depression.Frontiers in psychiatry · 2024Article
- Natural language processing applied to mental illness detection: a narrative review.NPJ digital medicine · 2022Review
- An analysis of patient motivation for seeking online treatment for binge eating disorder-A mixed methods study combining systematic text condensation with sentiment analysis.Frontiers in psychiatry · 2022Article
- An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM.Multimedia tools and applications · 2022Article
- Article
- Understanding the Life, Pain, and Barriers to Physical Activity in Korean Patients with Panic Disorder: Photovoice Inquiry.International journal of environmental research and public health · 2021Article
- Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review.JMIR mental health · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Institute for Health Metrics and Evaluation (IHME) has stated that over 1.1 billion people suffered from mental disorders globally in 2016, and the burden of mental disorders has continued to grow with impacts on social development. Despite the implementation of strategies for promotion and prevention in mental health WHO's Comprehensive Mental Health Action Plan 2013⁻2020, the difficulty of diagnosis of mental disorders makes the objective "To provide comprehensive, integrated, and responsive mental health and social care services in community-based settings" hard to carry out. This paper presents a mental-disorder-aided diagnosis model (MDAD) to quantify the multipolarity sentiment affect intensity of users' short texts in social networks in order to analyze the 11-dimensional sentiment distribution. We searched the five mental disorder topics and collected data based on Twitter hashtag. Through sentiment distribution similarity calculations and Stochastic Gradient Descent (SGD), people with a high probability of suffering from mental disorder can be detected in real time. In particular, mental health warnings can be made in time for users with an obvious emotional tendency in their tweets. In the experiments, we make a comprehensive evaluation of MDAD by five common adult mental disorders: depressive disorder, anxiety disorder, obsessive-compulsive disorder (OCD), bipolar disorder, and panic disorder. Our proposed model can effectively diagnose common mental disorders by sentiment multipolarity analysis, providing strong support for the prevention and diagnosis of mental disorders.
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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.