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- W4386021053 abstract "Depression-driven suicide is a serious social problem. Early identification of depression is vital for the well-being of society. Clinical diagnosis of depression takes a significant amount of time and requires highly skilled medical staff, which greatly limits its accessibility. Social media analysis for depression detection is therefore a rapidly growing research area. However, most of the available methods can only detect the presence or absence of depression, not the severity of depression. On the other hand, a few recently developed models for depression severity detection have not been validated on large datasets due to fundamental issues such as data sparsity. In this study, we proposed a novel method based on confidence vectors for detecting the severity of depression. We evaluated our method using a large dataset consisting of more than 40,000 annotated statements extracted from multiple social network services. To our knowledge, this is the largest and most well-balanced dataset for depression severity classification to date. Preliminary results showed that our models outperformed the existing state-of-the-art models by 5%, achieving a micro-averaged F1 score of 66% for human depression severity detection." @default.
- W4386021053 created "2023-08-22" @default.
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- W4386021053 date "2023-01-01" @default.
- W4386021053 modified "2023-09-25" @default.
- W4386021053 title "HelaDepDet: A Novel Multi-class Classification Model for Detecting the Severity of Human Depression" @default.
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- W4386021053 doi "https://doi.org/10.1007/978-3-031-42141-9_1" @default.
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