A Deep Learning-based Depression Trend Analysis of Korean on Social Media |
Park, Seojeong
(Department of Library and Information Science, Yonsei University)
Lee, Soobin (Department of Library and Information Science, Yonsei University) Kim, Woo Jung (Department of Psychiatry, Yongin Severance Hospital, Yonsei University College of Medicine) Song, Min (Department of Library and Information Science, Yonsei University) |
1 | Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H., & Eichstaedt, J. C. (2017). Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences, 18, 43-49. https://doi.org/10.1016/j.cobeha.2017.07.005 DOI |
2 | KNU Korean Emotion Dictionary (2018, November 5). Available: https://github.com/park1200656/KnuSentiLex |
3 | Schwartz, H. A., Eichstaedt, J., Kern, M., Park, G., Sap, M., Stillwell, D., Kosinski, M., & Ungar, L. (2014). Towards assessing changes in degree of depression through facebook. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 118-125. https://doi.org/10.3115/v1/w14-3214 DOI |
4 | Athiwaratkun, B., Wilson, A. G., & Anandkumar, A. (2018). Probabilistic fasttext for multi-sense word embeddings. arXiv. https://doi.org/10.48550/arXiv.1806.02901 |
5 | Yin, Z. & Shen, Y. (2018). On the dimensionality of word embedding. arXiv preprint arXiv:1812.04224. https://doi.org/10.48550/arXiv.1812.04224 |
6 | Zhang, L., Huang, X., Liu, T., Li, A., Chen, Z., & Zhu, T. (2014). Using linguistic features to estimate suicide probability of Chinese microblog users. In International Conference on Human Centered Computing, 549-559. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_45 DOI |
7 | Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K. W. (2018). Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496. https://doi.org/10.18653/v1/d18-1521 |
8 | Aizawa, A. (2003). An information-theoretic perspective of tf-idf measures. Information Processing & Management, 39(1), 45-65. http://doi.org/10.1109/ICHI.2018.00058 DOI |
9 | Alessa, A., Faezipour, M., & Alhassan, Z. (2018). Text classification of flu-related tweets using fasttext with sentiment and keyword features. In 2018 Institute of Electrical and Electronics Engineers International Conference on Healthcare Informatics (ICHI), 366-367. http://doi.org/10.1109/ICHI.2018.00058 DOI |
10 | Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008 DOI |
11 | Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191-235. https://doi/org/10.1177/053901883022002003 DOI |
12 | Lilleberg, J., Zhu, Y., & Zhang, Y. (2015). Support vector machines and word2vec for text classification with semantic features. In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC), 136-140. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCI-CC.2015.7259377 DOI |
13 | Conway, M. & O'Connor, D. (2016). Social media, big data, and mental health: current advances and ethical implications. Current Opinion in Psychology, 9, 77-82. https://doi.org/10.1016/j.copsyc.2016.01.004 DOI |
14 | Coppersmith, G., Dredze, M., Harman, C., Hollingshead, K., & Mitchell, M. (2015). CLPsych 2015 shared task: Depression and PTSD on Twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 31-39. https://doi.org/10.3115/v1/w15-1204 DOI |
15 | De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., & Kumar, M. (2016). Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2098-2110. https://doi.org/10.1145/2858036.2858207 DOI |
16 | Al Essa, A. (2018). Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection. Doctoral dissertation, University of Bridgeport. |
17 | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022. https://doi.org/10.1016/b978-0-12-411519-4.00006-9 DOI |
18 | Lalithamani, N., Thati, L. S., & Adhikesavan, R. (2014). Sentence level sentiment polarity calculation for customer reviews by considering complex sentential structures. IJRET: International Journal of Research in Engineering and Technology, 3(3), 433-438. https://doi.org/10.15623/ijret.2014.0303081 DOI |
19 | Liang, H., Sun, X., Sun, Y., & Gao, Y. (2017). Text feature extraction based on deep learning: a review. EURASIP Journal on Wireless Communications and Networking, 2017(1), 1-12. https://doi.org/10.1186/s13638-017-0993-1 DOI |
20 | Liu, G. & Guo, J. (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325-338. https://doi.org/10.1016/j.neucom.2019.01.078 DOI |
21 | Martin, L., Muller, B., Suarez, P. J. O., Dupont, Y., Romary, L., de la Clergerie, E. V., Seddah, D., & Sagot, B. (2019). Camembert: a tasty french language model. https://doi.org/10.18653/v1/2020.acl-main.645 |
22 | Resnik, P., Garron, A., & Resnik, R. (2013). Using topic modeling to improve prediction of neuroticism and depression in college students. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 1348-1353. url: https://www.aclweb.org/anthology/D13-1133 |
23 | Nam, K. K., Ackerman, M. S., & Adamic, L. A. (2009). Questions in, knowledge in? A study of Naver's question answering community. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 779-788. https://doi.org/10.1145/1518701.1518821 DOI |
24 | Pasupa, K. & Ayutthaya, T. S. N. (2019). Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features. Sustainable Cities and Society, 50, 101615. https://doi.org/10.1016/j.scs.2019.101615 DOI |
25 | Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532-1543. https://doi.org/10.3115/v1/D14-1162 DOI |
26 | Yun-tao, Z., Ling, G., & Yong-cheng, W. (2005). An improved TF-IDF approach for text classification. Journal of Zhejiang University-Science A, 6(1), 49-55. https://doi.org/10.1007/BF02842477 DOI |
27 | Resnik, P., Armstrong, W., Claudino, L., Nguyen, T., Nguyen, V. A., & Boyd-Graber, J. (2015). Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 99-107. https://doi.org/10.3115/v1/w15-1212 DOI |
28 | Lim, J. H., Kim, H. K., & Kim, Y. K. (2020). Recent R&D trends for pretrained language model. Electronics and Telecommunications Trends, 35(3), 9-19. https://doi.org/10.22648/ETRI.2020.J.350302 DOI |
29 | Moon, E. & Han, S. (2011). A qualitative method to find influencers using similarity-based approach in the blogosphere. International Journal of Social Computing and Cyber-Physical Systems, 1(1), 56-78. https://doi.org/10.1504/ijsccps.2011.043604 DOI |
30 | Petterson, J., Smola, A. J., Caetano, T. S., Buntine, W. L., & Narayanamurthy, S. M. (2010). Word features for latent dirichlet allocation. In NIPS, 1921-1929. https://doi.org/10.1.1.942.7045 |
31 | Lee G. (2019). Korean Ebedding. Korea: Acorn Publishing. |
32 | Ruas, T., Ferreira, C. H. P., Grosky, W., de Franca, F. O., & de Medeiros, D. M. R. (2020). Enhanced word embeddings using multi-semantic representation through lexical chains. Information Sciences, 532, 16-32. https://doi.org/10.1016/j.ins.2020.04.048 DOI |
33 | Coppersmith, G., Dredze, M., & Harman, C. (2014, June). Quantifying mental health signals in Twitter. In Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, 51-60. https://doi.org/10.3115/v1/w14-3207 DOI |
34 | Wang, Z. Y., Li, G., Li, C. Y., & Li, A. (2012). Research on the semantic-based co-word analysis. Scientometrics, 90(3), 855-875. https://doi.org/10.1007/s11192-011-0563-y DOI |
35 | Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv. http://arxiv.org/abs/1810.04805 |
36 | Kim Y. (2014). Convolutional neural networks for sentence classification. EMNLP2014-2014 Conference on Empirical Methods in Natural Language Processig, Association for Computational Linguistics, 1746-1751. https://doi.org/10.3115/v1/d14-1181 DOI |
37 | Cheng, C. H. & Chen, H. H. (2019). Sentimental text mining based on an additional features method for text classification. PloS One, 14(6), e0217591. https://doi.org/10.1371/journal.pone.0217591 DOI |
38 | Orabi, A. H., Buddhitha, P., Orabi, M. H., & Inkpen, D. (2018). Deep learning for depression detection of twitter users. In Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, 88-97. https://doi.org/10.18653/v1/W18-0609 DOI |
39 | Chronis, G. & Erk, K. (2020). When is a bishop not like a rook? When it's like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships. In Proceedings of the 24th Conference on Computational Natural Language Learning, 227-244. https://doi.org/10.18653/v1/2020.conll-1.17 DOI |
40 | Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE(Institute of Electrical and Electronics Engineers) Access, 7, 44883-44893. https://doi.org/10.1109/ACCESS.2019.2909180 DOI |
41 | Mowery, D., Smith, H., Cheney, T., Stoddard, G., Coppersmith, G., Bryan, C., & Conway, M. (2017). Understanding depressive symptoms and psychosocial stressors on Twitter: a corpus-based study. Journal of Medical Internet Research, 19(2), e48. https://doi.org/10.2196/jmir.6895 DOI |
42 | World Health Organization (2020). Available: https://www.who.int/health-topics/depression#tab=tab_1 |
43 | Trotzek, M., Koitka, S., & Friedrich, C. M. (2018). Early detection of depression based on linguistic metadata augmented classifiers revisited. In International Conference of the Cross-Language Evaluation Forum for European Languages, 191-202. Springer, Cham. https://doi.org/10.1007/978-3-319-98932-7_18 DOI |
44 | Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015). Recognizing depression from twitter activity. In Proceedings of the 33rd annual ACM conference on Human Factors in Computing Systems, 3187-3196. https://doi.org/10.1145/2702123.2702280 DOI |
45 | Turney, P. D. & Pantel, P. (2010). From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37, 141-188. https://doi.org/10.1613/jair.2934 DOI |
46 | Friedrich, M. J. (2017). Depression is the leading cause of disability around the world. Jama, 317(15), 1517-1517. https://doi.org/10.1001/jama.2017.3826 DOI |
47 | Qaiser, S. & Ali, R. (2018). Text mining: use of TF-IDF to examine the relevance of words to documents. International Journal of Computer Applications, 181(1), 25-29. https://doi.org/10.5120/ijca2018917395 DOI |