Abstract
Different mental disorders affect millions of people around the world, causing significant distress and interference to their daily life. Currently, the increased usage of social media platforms, where people share personal information about their day and problems, opens up new opportunities to actively detect these problems. We present a new approach inspired in the modeling of fine-grained emotions expressed by the users and deep learning architectures with attention mechanisms for the detection of depression and anorexia. With this approach, we improved the results over traditional and deep learning techniques. The use of attention mechanisms helps to capture the important sequences of fine-grained emotions that represent users with mental disorders.
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Notes
- 1.
We select N empirically, testing recommended sizes of sequences in the literature of 25, 35, 50 and 100.
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Acknowledgments
This research was supported by CONACyT-Mexico (Scholarship 654803 and Project CB-2015-01-257383).
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Aragón, M.E., López-Monroy, A.P., González, L.C., Montes-y-Gómez, M. (2020). Attention to Emotions: Detecting Mental Disorders in Social Media. In: Sojka, P., Kopeček, I., Pala, K., Horák, A. (eds) Text, Speech, and Dialogue. TSD 2020. Lecture Notes in Computer Science(), vol 12284. Springer, Cham. https://doi.org/10.1007/978-3-030-58323-1_25
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