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.
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References
Aguilera, J., Farías, D.I.H., Ortega-Mendoza, R.M., Montes-y Gómez, M.: Depression and anorexia detection in social media as a one-class classification problem. Appl. Intell., 1–16 (2021)
Burdisso, S.G., Errecalde, M., Montes-y Gómez, M.: A text classification framework for simple and effective early depression detection over social media streams. Expert Syst. Appl. 133, 182–197 (2019)
Chen, T., Xu, R., He, Y., Wang, X.: Improving sentiment analysis via sentence type classification using BILSTM-CRF and CNN. Expert Syst. Appl. 72, 221–230 (2017)
Chen, X., Hai, Z., Wang, S., Li, D., Wang, C., Luan, H.: Metaphor identification: a contextual inconsistency based neural sequence labeling approach. Neurocomputing 428, 268–279 (2021)
Gong, H., Gupta, K., Jain, A., Bhat, S.: Illinimet: illinois system for metaphor detection with contextual and linguistic information. In: Proceedings of the Second Workshop on Figurative Language Processing, pp. 146–153 (2020)
Guo, B., Zhang, C., Liu, J., Ma, X.: Improving text classification with weighted word embeddings via a multi-channel CNN model. Neurocomputing 363, 366–374 (2019)
Gutierrez, E.D., Cecchi, G.A., Corcoran, C., Corlett, P.: Using automated metaphor identification to aid in detection and prediction of first-episode schizophrenia. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. pp. 2923–2930 (2017)
Hua, Q., Qundong, S., Dingchao, J., Lei, G., Yanpeng, Z., Pengkang, L.: A character-level method for text classification. In: 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) pp. 402–406. IEEE (2018)
Ive, J., Gkotsis, G., Dutta, R., Stewart, R., Velupillai, S.: Hierarchical neural model with attention mechanisms for the classification of social media text related to mental health. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp. 69–77 (2018)
Ji, S., Li, X., Huang, Z., Cambria, E.: Suicidal ideation and mental disorder detection with attentive relation networks. arXiv preprint arXiv:2004.07601 (2020)
Kim, Y.: Convolutional Neural Networks for Sentence Classification. arXiv e-prints arXiv:1408.5882, August 2014
Kumar, A., Sharma, K., Sharma, A.: Hierarchical deep neural network for mental stress state detection using iot based biomarkers. Pattern Recognition Letters (2021)
Llewellyn-Beardsley, J., et al.: Characteristics of mental health recovery narratives: systematic review and narrative synthesis. PloS one 14(3), (2019)
Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk: early risk prediction on the internet. In: Bellot, P., et al. (eds.) CLEF 2018. LNCS, vol. 11018, pp. 343–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98932-7_30
Magaña, D.: Cultural competence and metaphor in mental healthcare interactions: a linguistic perspective. Patient Educ. Couns. 102(12), 2192–2198 (2019)
Mao, R., Lin, C., Guerin, F.: End-to-end sequential metaphor identification inspired by linguistic theories. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3888–3898 (2019)
Preotiuc-Pietro, D., Sap, M., Schwartz, H.A., Ungar, L.H.: Mental illness detection at the world well-being project for the clpsych 2015 shared task. In: CLPsych@ HLT-NAACL, pp. 40–45 (2015)
Rivera, A.T., Oliver, A., Climent, S., Coll-Florit, M.: Neural metaphor detection with a residual BILSTM-CRF model. In: Proceedings of the Second Workshop on Figurative Language Processing, pp. 197–203 (2020)
Saravia, E., Chang, C.H., De Lorenzo, R.J., Chen, Y.S.: Midas: mental illness detection and analysis via social media. In: 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1418–1421. IEEE (2016)
Sekulić, I., Strube, M.: Adapting deep learning methods for mental health prediction on social media. arXiv preprint arXiv:2003.07634 (2020)
Steen, G.: A method for linguistic metaphor identification: From MIP to MIPVU, vol. 14. John Benjamins Publishing (2010)
Wilks, Y.: A preferential, pattern-seeking, semantics for natural language inference. In: Words and Intelligence I, pp. 83–102. Springer (2007)
Yao, L., Mao, C., Luo, Y.: Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med. Inform. Decis. Mak. 19(3), 71 (2019)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019) https://doi.org/10.1609/aaai.v33i01.33017370
Zhang, D., Lin, H., Liu, X., Zhang, H., Zhang, S.: Combining the attention network and semantic representation for Chinese verb metaphor identification. IEEE Access 7, 137103–137110 (2019)
Zhang, D., Zhang, M., Peng, C., Jung, J.J., Xia, F.: Metaphor research in the 21st century: a bibliographic analysis. Comput. Sci. Inf. Syst. 18(1), 303–321 (2021)
Zhang, P., Huang, X., Wang, Y., Jiang, C., He, S., Wang, H.: Semantic similarity computing model based on multi model fine-grained nonlinear fusion. IEEE Access 9, 8433–8443 (2021)
Zheng, J., Zheng, L.: A hybrid bidirectional recurrent convolutional neural network attention-based model for text classification. IEEE Access 7, 106673–106685 (2019)
Zhong, B., Huang, Y., Liu, Q.: Mental health toll from the coronavirus: social media usage reveals wuhan residents’ depression and secondary trauma in the covid-19 outbreak. Computers in human behavior 114, 106524 (2021)
Zirikly, A., Resnik, P., Uzuner, Ö., Hollingshead, K.: CLPsych 2019 shared task: Predicting the degree of suicide risk in Reddit posts. In: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, pp. 24–33. Association for Computational Linguistics, Minneapolis, Minnesot, June 2019. https://doi.org/10.18653/v1/W19-3003, https://www.aclweb.org/anthology/W19-3003
Acknowledgment
This work is partially supported by National Natural Science Foundation of China under Grants No. 62076051.
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Zhang, D., Shi, N., Peng, C., Aziz, A., Zhao, W., Xia, F. (2021). MAM: A Metaphor-Based Approach for Mental Illness Detection. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_47
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