Deep Bag-of-Sub-Emotions for Depression Detection in Social Media
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-83527-9_5
Reference22 articles.
1. Aragón, M.E., López-Monroy, A.P., González-Gurrola, L.C., Gómez, M.M.: Detecting depression in social media using fine-grained emotions. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019)
2. Bromet, R.K.E., Jonge, P., Shahly, V., Wilcox, M.: The burden of depressive illness. In: Public Health Perspectives on Depressive Disorders (2017)
3. Cong, Q., Feng, Z., Li, F., Xiang, Y., Rao, G., Tao, C.: XA-BiLSTM: a deep learning approach for depression detection in imbalanced data. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1624–1627. IEEE (2018)
4. Coppersmith, G., Ngo, K., Leary, R., Wood, A.: Exploratory analysis of social media prior to a suicide attempt. In: Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology, pp. 106–117 (2016)
5. De Choudhury, M., Counts, S., Horvitz, E.J., Hoff, A.: Characterizing and predicting postpartum depression from shared Facebook data. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 626–638 (2014)
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