- Volume 9 Issue 7
DOI QR Code
Inference of Korean Public Sentiment from Online News
온라인 뉴스에 대한 한국 대중의 감정 예측
- Matteson, Andrew Stuart (Dept. of Computer Science and Engineering, Korea University) ;
- Choi, Soon-Young (Dept. of Computer Science and Engineering, Korea University) ;
- Lim, Heui-Seok (Dept. of Computer Science and Engineering, Korea University)
- Received : 2018.04.25
- Accepted : 2018.07.20
- Published : 2018.07.28
Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.
Sentiment Analysis;Crowdsourcing;Online News;Emotion Dictionary;Social Emotion Detection;Natural Language Processing
Supported by : National Research Foundation of Korea (NRF)
- K. H. Lin, C. Yang & H. H. Chen. (2007). What Emotions do News Articles Trigger in their Readers? Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 38(6), 733-734.
- J. S. Song & S. W. Lee (2011). Automatic Construction of Positive/Negative Feature-Predicate Dictionary for Polarity Classification of Product Reviews. Journal of KIISE: Software and Applications, 38(3), 157-168.
- J. S. Myung, D. J Lee & S. G. Lee (2007). A Korean Product Review Analysis System Using a Semi-Automatically Constructed Semantic Dictionary. Proceedings of the 19th Annual Conference on Human and Cognitive Language Technology, 68-75.
- C. H. Jeong, J. H. Kim, Y. J. Jeon & H. J. Jeong (2017). Korean Sentiment Dictionary Based on the Reliability of Review Data. Journal of Korean Institute of Information Scientists and Engineers, 1965-1967.
- J. H. Seo, J. H Cho & J. T. Choi (2015). Design for Opinion Dictionary of Emotion Applying Rules for Antonym of the Korean Grammar. Journal of Advanced Information Technology and Convergence, 13(2), 109-117.
- X. Fang & J. Zhan. (2015). Sentiment Analysis Using Product Review Data. Journal of Big Data, 2(1), 5. https://doi.org/10.1186/s40537-015-0015-2
- B. Pang, L. Lee & S. Vaithyanahtan (2002). Thumbs Up?: Sentiment Classification using Machine Learning Techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, volume 10, 79-86.
- A. Pak & P. Paroubek (2010). Twitter as a Corpus for Sentiment Analysis and Opinion Mining. LREc, 10.
- L. Qu, G. Ifrim & G. Weikum (2010). The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns. Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics, 913-921.
- S. Baccianella, A. Esuli & F. Sebastiani. (2009). Multi-Facet Rating of Product Reviews. European Conference on Information Retrieval, 461-472.
- J. S. Kim (2016). Emotion Prediction of Paragraph using Big Data Analysis. Korea Convergence Society, 14(11), 267-273.
- P. Katz, M. Singleton & R. Wicentowski. (2007). SWAT-MP: The SemEval-2007 Systems for Task 5 and Task 14. Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics, 308-313.
- C. Strapparava & R. Mihalcea (2007). SemEval-2007 Task 14: Affective Text. Proceedings of the 4th International Workshop on Semantic Evaluations. Association for Computational Linguistics, 70-74.
- Y. Rao, J. Lei, L. Wenyin, Q. Li & M. Chen (2014). Building Emotional Dictionary for Sentiment Analysis of Online News. World Wide Web, 17(4), 723-742. https://doi.org/10.1007/s11280-013-0221-9
- J. O. Kim, S. S Lee & H. S. Yong. (2011). Automatic Classification Scheme of Opinions Written in Korean. Journal of Korean Institute of Information Scientists and Engineers, 38(6), 423-428.
- Y. A. Heo, D. Y. Lee & G. G. Kim (2017). A System for Automatic Classification of Traditional Texts. Korea Convergence Society, 8(12), 39-47.
- Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., Yu, Y (2011). Mining Social Emotions from Affective Text. IEEE Transactions on Knowledge and Data Engineering, 24, 1658-1670.
- S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han & Y. Yu, (2009). Joint Emotion-Topic Modeling for Social Affective Text Mining. Proceedings of the 9th IEEE International Conference on Data Mining, 699-704.