- Title
- MAM : a metaphor-based approach for mental illness detection
- Creator
- Zhang, Dongyu; Shi, Nan; Peng, Ciyuan; Aziz, Abdul; Zhao, Wenhong; Xia, Feng
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179129
- Identifier
- vital:15530
- Identifier
-
https://doi.org/10.1007/978-3-030-77967-2_47
- Identifier
- ISBN:03029743 (ISSN); 9783030779665 (ISBN)
- Abstract
- Among the most disabling disorders, mental illness is one that affects millions of people across the world. Although a great deal of research has been done to prevent mental disorders, detecting mental illness in potential patients remains a considerable challenge. This paper proposes a novel metaphor-based approach (MAM) to determine whether a social media user has a mental disorder or not by classifying social media texts. We observe that the social media texts posted by people with mental illness often contain many implicit emotions that metaphors can express. Therefore, we extract these texts’ metaphor features as the primary indicator for the text classification task. Our approach firstly proposes a CNN-RNN (Convolution Neural Network - Recurrent Neural Network) framework to enable the representations of long texts. The metaphor features are then applied to the attention mechanism for achieving the metaphorical emotions-based mental illness detection. Subsequently, compared with other works, our approach achieves creative results in the detection of mental illnesses. The recall scores of MAM on depression, anorexia, and suicide detection are the highest, with 0.50, 0.70, and 0.65, respectively. Furthermore, MAM has the best F1 scores on depression and anorexia detection tasks, with 0.51 and 0.71. © 2021, Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- 21st International Conference on Computational Science, ICCS 2021 Vol. 12744 LNCS, p. 570-583
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ Springer Nature Switzerland AG 2021
- Rights
- Open Access
- Subject
- Attention model; Mental illness; Metaphor; Text classification
- Full Text
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