과제정보
이 논문은 2022년도 인하공업전문대학 학술연구사업 지원에 의하여 연구되었음
참고문헌
- Daniel, P.P., G. Mihaela and A. Nikolaos, 2019, Automatically identifying complaints in social media, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, July 28 - August 2, pp.5008-5019.
- Friendly, M. 2006. A brief history of data visualization. In: C. Chen et al.(ed.). Handbook of Data Visualization. Springer Handbooks Computational Statistics. Springer, Berlin, Heidelberg, pp.15-56.
- Friendly, M. 2009. Milestones in the history of thematic cartography, statistical graphics, and data visualization. http://www.math.usu.edu/~symanzik/teaching/2009_stat6560/Downloads/Friendly_milestone.pdf
- Hong, S.E. 2018. Sentimental & pattern analysis of environment complaint by big data mining. Inha University. Korea. 89pp
- Hwang, S.W. 2019. A study on the influence of happiness score on the movement of residence in the noise complaints: Focused on the complaint board in 25 districts of Seoul. Yonsei University. Korea. 75pp
- Jeon, S. E and D. B. Shin, 2018, A study on the agent based infection prediction model using space big data -focusing on MERS-CoV incident in Seoul. Journal of the Korean Association of Geographic Information Studies 21(2):94-106
- Jin, H. I., G.Y. Ock and M.s. Lee, 2020, A study on a methodology to measure happiness index with social big data: Focusing on Google Trends. Korean Journal of Korea society of innovation 15(5):215-237 https://doi.org/10.46251/INNOS.2020.12.15.5.215
- Kim, D.H. and D. Kim, 2018, Development and application of dynamic visualization model for spatial big data. Journal of the Korean Association of Geographic Information Studies 21(1):57-70
- Mikolov, T., W. T. Yih, and G. Zweig. 2018. Deconfounded lexicon induction for interpretable social science. In Proceedings of the 2018 Annual Conference of the North American Chapter of the Association for Computational, pp 1615-1625.
- Yang, W., L. Tan, C. Lu, A. Cui, H. Li, X. Chen, K. Xiong, M. Wang, M. Li, J. Pei, and J. Lin. 2019. Detecting customer complaint escalation with recurrent neural networks and manually-engineered features. In Proceedings of the 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Industry Track), NAACL, pp. 56-63.
- Zhou, G. and K. Ganesan. 2016. Linguistic understanding of complaints and praises in user reviews. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), NAACL, pp. 109-114.