Automatic anxiety recognition method based on microblog text analysis
Frontiers in Public Health, ISSN: 2296-2565, Vol: 11, Page: 1080013
2023
- 4Citations
- 22Captures
- 1Mentions
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Most Recent News
Research from Chinese Academy of Sciences Provides New Data on Anxiety Disorders (Automatic anxiety recognition method based on microblog text analysis)
2023 APR 04 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Daily -- Researchers detail new data in anxiety disorders. According
Article Description
Mental health has traditionally been assessed using a self-report questionnaire. Although this approach produces accurate results, it has the disadvantage of being labor-intense and time-consuming. This study aimed to extract original text information published by users on the social media platform (Sina Weibo). A machine learning method was used to train the model and predict the anxiety state of the user automatically. Data of 1,039 users were collected. First, Weibo users were invited to fill the anxiety self-assessment scale. All original text data ever published by the users were collected. Second, the Simplified Chinese-Linguistic Inquiry and Word Count (SC-LIWC) were extracted for feature selection and model training. We found that the model achieved the best performance when the XGBoostRegressor algorithm was used. The Pearson correlation coefficient between the model predicted scores and self-reported scores was moderate (r = 0.322). In addition, we tested the reliability of the model, and found that the model had high reliability (r = 0.72). The experimental results further showed that the model was feasible and effective and could use the digital footprints to predict psychological characteristics.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85151791231&origin=inward; http://dx.doi.org/10.3389/fpubh.2023.1080013; http://www.ncbi.nlm.nih.gov/pubmed/37020823; https://www.frontiersin.org/articles/10.3389/fpubh.2023.1080013/full; https://dx.doi.org/10.3389/fpubh.2023.1080013; https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1080013/full
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