A Deep Learning-based Depression Trend Analysis of Korean on Social Media

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참고문헌 (47)

1. Aizawa, A., "An information-theoretic perspective of tf–idf measures", Information Processing & Management, 39(1), pp.45~65 (2003)[DOI]
2. Al Essa, A., "Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection", Doctor, pp.2018
3. Alessa, A., "Text classification of flu-related tweets using fasttext with sentiment and keyword features", 2018 Institute of Electrical and Electronics Engineers International Conference on Healthcare Informatics (ICHI), pp.366~367 (2018)
4. "Probabilistic fasttext for multi-sense word embeddings" (2018)[URL]
5. Blei, D. M., "Latent dirichlet allocation", The Journal of Machine Learning Research, 3, pp.993~1022 (2003)[DOI]
6. Vincent D Blondel, "Fast unfolding of communities in large networks", Journal of Statistical Mechanics: Theory and Experiment, 2008(10), pp.P10008~ (2008), IOP Publishing[DOI]
7. Callon, M., "From translations to problematic networks : An introduction to co-word analysis", Social Science Information, 22(2), pp.191~235 (1983)[DOI]
8. Ching-Hsue Cheng, "Sentimental text mining based on an additional features method for text classification", PLOS ONE, 14(6), pp.e0217591~ (2019), Public Library of Science (PLoS)[DOI]
9. Chronis, G., "When is a bishop not like a rook? When it’s like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships", Proceedings of the 24th Conference on Computational Natural Language Learning, pp.227~244 (2020)
10. Conway, M., "Social media, big data, and mental health : current advances and ethical implications", Current Opinion in Psychology, 9, pp.77~82 (2016)[DOI]
11. Coppersmith, G., "Quantifying mental health signals in Twitter", Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp.51~60 (2014)
12. Coppersmith, G., "CLPsych 2015 shared task: Depression and PTSD on Twitter", Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp.31~39 (2015)
13. De Choudhury, M., "Discovering shifts to suicidal ideation from mental health content in social media", Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp.2098~2110 (2016)
14. "Bert: Pre-training of deep bidirectional transformers for language understanding" (2018)[URL]
15. Friedrich, M. J., "Depression is the leading cause of disability around the world", Jama, 317(15), pp.1517~1517 (2017)[DOI]
16. Guntuku, S. C., "Detecting depression and mental illness on social media : an integrative review", Current Opinion in Behavioral Sciences, 18, pp.43~49 (2017)[DOI]
17. Kim Y., "Convolutional neural networks for sentence classification", EMNLP2014-2014 Conference on Empirical Methods in Natural Language Processig, Association for Computational Linguistics, pp.1746~1751 (2014)
18. "KNU Korean Emotion Dictionary" (2018)[URL]
19. Lalithamani, N., "Sentence level sentiment polarity calculation for customer reviews by considering complex sentential structures", IJRET : International Journal of Research in Engineering and Technology, 3(3), pp.433~438 (2014)[DOI]
20. Lee G., "Korean Ebedding" (2019), Acorn Publishing
21. Liang, H., "Text feature extraction based on deep learning : a review", EURASIP Journal on Wireless Communications and Networking, 2017(1), pp.1~12 (2017)[DOI]
22. Lilleberg, J., "Support vector machines and word2vec for text classification with semantic features", 2015 IEEE 14th International Conference on Cognitive Informatics &Cognitive Computing (ICCI* CC), pp.136~140 (2015)
23. Lim, J. H., "Recent R&D trends for pretrained language model", Electronics and Telecommunications Trends, 35(3), pp.9~19 (2020)[DOI]
24. Gang Liu, "Bidirectional LSTM with attention mechanism and convolutional layer for text classification", Neurocomputing, 337, pp.325~338 (2019), Elsevier BV[DOI]
25. Louis Martin, "CamemBERT: a Tasty French Language Model", Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020), Association for Computational Linguistics
26. Eunyoung Moon, "A qualitative method to find influencers using similarity-based approach in the blogosphere", International Journal of Social Computing and Cyber-Physical Systems, 1(1), pp.56~78 (2011), Inderscience Publishers[DOI]
27. Danielle Mowery, "Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study", Journal of Medical Internet Research, 19(2), pp.e48~ (2017), JMIR Publications Inc.[DOI]
28. Nam, K. K., "Questions in, knowledge in? A study of Naver’s question answering community", Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp.779~788 (2009)
29. Orabi, A. H., "Deep learning for depression detection of twitter users", Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic, pp.88~97 (2018)
30. Kitsuchart Pasupa, "Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features", Sustainable Cities and Society, 50, pp.101615~ (2019), Elsevier BV[DOI]
31. Pennington, J., "Glove: Global vectors for word representation", Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1532~1543 (2014)
32. Petterson, J., "Word features for latent dirichlet allocation", pp.1921~1929 (2010), NIPS
33. Shahzad Qaiser, "Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents", International Journal of Computer Applications, 181(1), pp.25~29 (2018), Foundation of Computer Science[DOI]
34. Resnik, P., "Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter", Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp.99~107 (2015)
35. Resnik, P., "Using topic modeling to improve prediction of neuroticism and depression in college students", Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp.1348~1353 (2013)
36. Ruas, T., "Enhanced word embeddings using multi-semantic representation through lexical chains", Information Sciences, 532, pp.16~32 (2020)[DOI]
37. Schwartz, H. A., "Towards assessing changes in degree of depression through facebook", Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, pp.118~125 (2014)
38. Tadesse, M. M., "Detection of depression-related posts in reddit social media forum", IEEE(Institute of Electrical and Electronics Engineers)Access, 7, pp.44883~44893 (2019)[DOI]
39. Trotzek, M., "Early detection of depression based on linguistic metadata augmented classifiers revisited", International Conference of the Cross-Language Evaluation Forum for European Languages, pp.191~202 (2018), Springer
40. Tsugawa, S., "Recognizing depression from twitter activity", Proceedings of the 33rd annual ACM conference on Human Factors in Computing Systems, pp.3187~3196 (2015)
41. Turney, P. D., "From frequency to meaning : Vector space models of semantics", Journal of Artificial Intelligence Research, 37, pp.141~188 (2010)[DOI]
42. Wang, Z. Y., "Research on the semantic-based co-word analysis", Scientometrics, 90(3), pp.855~875 (2012)[DOI]
43. "World Health Organization" (2020)[URL]
44. "On the dimensionality of word embedding. arXiv preprint arXiv:1812.04224" (2018)[URL]
45. Yun-tao, Z., "An improved TF-IDF approach for text classification", Journal of Zhejiang University-Science A, 6(1), pp.49~55 (2005)[DOI]
46. Zhang, L., "Using linguistic features to estimate suicide probability of Chinese microblog users", International Conference on Human Centered Computing, pp.549~559 (2014), Springer
47. "Learning gender-neutral word embeddings. arXiv preprint arXiv:1809.01496" (2018)[URL]

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