최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기정보관리학회지 = Journal of the Korean society for information management, v.36 no.4, 2019년, pp.207 - 226
서하림 (연세대학교 문헌정보학과) , 송민 (연세대학교 문헌정보학과)
Depression is a serious psychological disease that is expected to afflict an increasing number of people. And studies on depression have been conducted in the context of social media because social media is a platform through which users often frankly express their emotions and often reveal their me...
Kim, Jae-bong, & Kim, Hyung-Joong (2017). A domain-specific sentiment lexicon construction method for stock index directionality. Digital Contents Society, 18(3), 585-592.
Seo, Sang-hyeon, & Kim, Jun-Tae (2016). Deep learning based sentiment analysis research trends. Korea Multimedia Society, 3, 8-22.
Song, Min (2017). Text mining. Seoul: Chungram Books.
Song, Ho-Yun, Park, Han-chul, Yang, Won-seok, & Park, Jong-chul (2017). Predicting symptoms of depression for social media users via linguistic patterns. Korea Information Science Society, 625-627.
Lee, Yu-Lim (2016). Pharmaceuticalization of emotion and structuring depression as experience: An analysis of the depression experiences of Korean women in their 20s. Korea Womens Studies Institute, 16(1), 81-117.
Lee, Hyun-Seo, & Song, Min (2018). An analysis of the influences between sentiment values of korean online news and macroeconomic indicators using text mining. Journal of Communication Science, 18(3), 129-169. https://doi.org/10.14696/jcs.2018.09.18.3.129
Chang, Jae-Young (2009). A sentiment analysis algorithm for automatic product reviews classification in on-line shopping mall. The Journal of Society for e-Business Studies, 14(4), 19-33.
Bastian, Heymann, & Jacomy (2009). Gephi: An open source software for exploring and manipulating networks. Icwsm, 361-362.
Coppersmith, G., Dredze, M., & Harman, C. (2014). Quantifying mental health signals in twitter. 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
De Choudhury, M. C. (2013). Social media as a measurement tool of depression in populations. Proceedings of the 5th Annual ACM Web Science Conference, 47-56. ACM. https://doi.org/10.1145/2464464.2464480
De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. ICWSM, 13, 1-10.
Hammen, C., & Brennan, P. A. (2002). Interpersonal dysfunction in depressed women: Impairments independent of depressive symptoms. Journal of Affective Disorders, 72(2), 145-156. https://doi.org/10.1016/s0165-0327(01)00455-4
Jiang, L., & Yang, C. C. (2013). Using co-occurrence analysis to expand consumer health vocabularies from social media data. IEEE International Conference on Healthcare Informatics, 75-81. https://doi.org/10.1109/ichi.2013.16
Mimno, D., & McCallum, A. (2008). Topic models conditioned on arbitrary features with dirichlet-multinomial regression. The 24th Conference on Unvertainty in Artificial Intelligence, 411-418. Helsinki: UAI.
NAMI. (2014). Tell me about depression. Retrieved from NAMI - National Alliance on Mental Illness: https://www.nami.org/Learn-More/Mental-Health-Conditions/Depression
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Proceedings of the ACL-02 conference on Empirical methods in natural language processing, 10, 79-86. Association for Computational Linguistics.
Park, M., Cha, C., & Cha, M. (2012). Depressive moods of users portrayed in twitter. Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics, 1-8. New York: ACM.
SHINEWARE. (2013. 3. 20). Products-komoran. Retrieved from SHINEWARE: https://www.shineware.co.kr/products/komoran/
Snow, R., O'Connor, B., Jurafsky, D., & Ng, Y. A. (2008). Cheap and fast---but is it good?: evaluating non-expert annotations for natural language tasks. Proceedings of the Conference on Empirical Methods in Natural Language Processing, 254-263. Association for Computational Linguistics. https://doi.org/10.3115/1613715.1613751
Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. Proceedings of the ACL 2012 System Demonstrations, 115-120. Association for Computational Linguistics.
Wang, Z. (2017). Machine learning methods for finding textual features of depression from publications. Georgia: Georgia State University.
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., & Li, X. (2011). Comparing twitter and traditional media using topic models. European conference on information retrieval, 338-349. Berlin: Springer.
Zhou, Z., Wang, W., & Wang, L. (2012). Community detection based on an improved modularity. Springer, 638-645. Berlin: Heidelberg. https://doi.org/10.1007/978-3-642-33506-8_78
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.