Xiaojun Quan
Skip Abstract Section
Abstract
Sentiment analysis of such opinionated online texts as reviews and comments has received increasingly close attention, yet most of the work is intended to deal with the detection of authors’ emotion. In contrast, this article presents our study of the social emotion detection problem, the objective of which is to identify the evoked emotions of readers by online documents such as news articles. A novel Latent Discriminative Model (LDM) is proposed for this task. LDM works by introducing intermediate hidden variables to model the latent structure of input text corpora. To achieve this, it defines a joint distribution over emotions and latent variables, conditioned on the observed text documents. Moreover, we assume that social emotions are not independent but correlated with one another, and the dependency of them is capable of providing additional guidance to LDM in the training process. The inclusion of this emotional dependency into LDM gives rise to a new Emotional Dependency-based LDM (eLDM). We evaluate the proposed models through a series of empirical evaluations on two real-world corpora of news articles. Experimental results verify the effectiveness of LDM and eLDM in social emotion detection.
- C. O. Alm, D. Roth, and R. Sproat. 2005. Emotions from text: Machine learning for text-based emotion prediction. In Proceedings of the Joint Conference on Human Language Technology and Empirical Methods in Natural Language Processing. 579--586. Google Scholar
Digital Library
- T. L. Bailey and C. Elkan. 1995. Unsupervised learning of multiple motifs in biopolymers using expectation maximization. Machine Learning 21, 1 (1995), 51--80. Google ScholarDigital Library
- S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han, and Y. Yu. 2012. Mining social emotions from affective text. IEEE Transactions on Knowledge and Data Engineering 24, 9 (2012), 1658--1670. Google ScholarDigital Library
- Christopher M. Bishop and others. 2006. Pattern Recognition and Machine Learning. Vol. 1. Springer New York. Google ScholarDigital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3 (2003), 993--1022. Google ScholarDigital Library
- D. Bollegala, D. Weir, and J. Carroll. 2011. Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 132--141. Google ScholarDigital Library
- Johan Bollen, Huina Mao, and Xiaojun Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (2011), 1--8.Google ScholarCross Ref
- C. C. Chang and C. J. Lin. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent System Technology 2, 3 (2011), 1--27. Google ScholarDigital Library
- P. C. Chang, M. Galley, and C. Manning. 2008. Optimizing Chinese word segmentation for machine translation performance. In Proceedings of the ACL Workshop on Statistical Machine Translation. 224--232. Google ScholarDigital Library
- J. A. Chevalier and D. Mayzlin. 2006. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43, 3 (2006), 345--354.Google ScholarCross Ref
- A. P. Dempster, N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1 (1977), 1--38.Google Scholar
- W. Duan, B. Gu, and A. B. Whinston. 2008. Do online reviews matter?--An empirical investigation of panel data. Decision Support Systems 45, 4 (2008), 1007--1016. Google ScholarDigital Library
- P. Ekman. 1992. An argument for basic emotions. Cognition & Emotion 6, 3--4 (1992), 169--200.Google ScholarCross Ref
- P. Ekman and W. V. Friesen. 1969. The repertoire of nonverbal behavior: Categories, origins, usage, and coding. Semiotica 1 (1969), 49--98.Google ScholarCross Ref
- R. E. Fan, K. W, Chang, C. J. Hsieh, X. R. Wang, and C. J. Lin. 2008. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research 9 (2008), 1871--1874. Google ScholarDigital Library
- C. Fellbaum. 1999. WordNet. Wiley Online Library.Google Scholar
- Y. Freund and R. E. Schapire. 1996. Experiments with a new boosting algorithm. In Proceedings of the International Conference on Machine Learning. 148--156.Google Scholar
- G. Fu, C. Kit, and J. J. Webster. 2008. Chinese word segmentation as morpheme-based lexical chunking. Information Sciences 178, 9 (2008), 2282--2296. Google ScholarDigital Library
- A. Ghose, P. G. Ipeirotis, and A. Sundararajan. 2007. Opinion mining using econometrics: A case study on reputation systems. In Proceedings of the Annual Meeting of the Association of Computational Linguistics. 416.Google Scholar
- T. L. Griffiths and M. Steyvers. 2004. Finding scientific topics. In Proceedings of the National Academy of Sciences of the United States of America. 5228--5235.Google Scholar
- M. Hu and B. Liu. 2004a. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 168--177. Google ScholarDigital Library
- M. Hu and B. Liu. 2004b. Mining opinion features in customer reviews. In Proceedings of the National Conference on Artificial Intelligence. 755--760. Google ScholarDigital Library
- Jingsheng Lei, Yanghui Rao, Qing Li, Xiaojun Quan, and Liu Wenyin. 2014. Towards building a social emotion detection system for online news. Future Generation Computer Systems 37 (2014), 438--448.Google ScholarDigital Library
- K. H. Y. Lin, C. Yang, and H. H. Chen. 2007. What emotions do news articles trigger in their readers. In Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 733--734. Google ScholarDigital Library
- K. H. Y. Lin, C. Yang, and H. H. Chen. 2008. Emotion classification of online news articles from the reader’s perspective. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 220--226. Google ScholarDigital Library
- Y. Liu. 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing 70, 3 (2006), 74--89.Google ScholarCross Ref
- Y. Lu, X. Kong, X. Quan, W. Liu, and Y. Xu. 2010. Exploring the sentiment strength of user reviews. In Web-Age Iformation Management. 471--482. Google ScholarDigital Library
- A. McCallum and K. Nigam. 1998. A comparison of event models for naive Bayes text classification. In AAAI-98 Workshop on Learning for Text Categorization. 41--48.Google Scholar
- A. Y. Ng. 2004. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the International Conference on Machine Learning. 78--85. Google ScholarDigital Library
- J. R. Nofsinger. 2005. Social mood and financial economics. The Journal of Behavioral Finance 6, 3 (2005), 144--160.Google ScholarCross Ref
- M. J. L. Orr. 1995. Regularization in the selection of radial basis function centers. Neural Computation 7, 3 (1995), 606--623. Google ScholarDigital Library
- A. Ortony and T. J. Turner. 1990. What’s basic about basic emotions? Psychological Review 97, 3 (1990), 315.Google ScholarCross Ref
- B. I. Page and R. Y. Shapiro. 1983. Effects of public opinion on policy. The American Political Science Review (1983), 175--190.Google Scholar
- B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Vol. 10. 79--86. Google ScholarDigital Library
- F. Peng, F. Feng, and A. McCallum. 2004. Chinese segmentation and new word detection using conditional random fields. In Proceedings of the International Conference on Computational Linguistics. Google ScholarDigital Library
- Yanghui Rao, Xiaojun Quan, Liu Wenyin, Qing Li, and Mingliang Chen. 2012. Building word-emotion mapping dictionary for online news. In Proceedings of the 1st International Workshop on Sentiment Discovery from Affective Data (SDAD). 28--39.Google Scholar
- E. Riloff and J. Wiebe. 2003. Learning extraction patterns for subjective expressions. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing. 105--112. Google ScholarDigital Library
- J. L. Rodgers and W. A. Nicewander. 1988. Thirteen ways to look at the correlation coefficient. The American Statistician 42, 1 (1988), 59--66.Google ScholarCross Ref
- C. Strapparava and R. Mihalcea. 2007. Semeval-2007 task 14: Affective text. In Proceedings of the International Workshop on Semantic Evaluations. 70--74. Google ScholarDigital Library
- C. Strapparava and R. Mihalcea. 2008. Learning to identify emotions in text. In Proceedings of the Annual ACM Symposium on Applied Computing. 1556--1560. Google ScholarDigital Library
- M. Thelwall, K. Buckley, G. Paltoglou, D. Cai, and A. Kappas. 2010. Sentiment strength detection in short informal text. Journal of the American Sociaty for Information Science and Technology 61, 12 (2010), 2544--2558. Google ScholarDigital Library
- Andranik Tumasjan, Timm Oliver Sprenger, Philipp G. Sandner, and Isabell M. Welpe. 2010. Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM 10 (2010), 178--185.Google ScholarCross Ref
- P. D. Turney. 2002. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the Annual Meeting on Association for Computational Linguistics. 417--424. Google ScholarDigital Library
- V. Vapnik. 1999. The Nature of Statistical Learning Theory (2nd. ed.). Springer Verlag. Google ScholarDigital Library
- J. Wiebe and E. Riloff. 2005. Creating subjective and objective sentence classifiers from unannotated texts. Computational Linguistics and Intelligent Text Processing (2005), 486--497. Google ScholarDigital Library
- T. Wilson, P. Hoffmann, S. Somasundaran, J. Kessler, J. Wiebe, Y. Choi, C. Cardie, E. Riloff, and S. Patwardhan. 2005. OpinionFinder: A system for subjectivity analysis. In Proceedings of HLT/EMNLP on Interactive Demonstrations. 34--35. Google ScholarDigital Library
- C. Yang, K. H. Y. Lin, and H. H. Chen. 2007. Emotion classification using web blog corpora. In Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence. 275--278. Google ScholarDigital Library
- Ying Zhang, Yi Fang, Xiaojun Quan, Lin Dai, Luo Si, and Xiaojie Yuan. 2012. Emotion tagging for comments of online news by meta classification with heterogeneous information sources. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1059--1060. Google ScholarDigital Library
- Ying Zhang, Ning Zhang, Luo Si, Yanshan Lu, Qifan Wang, and Xiaojie Yuan. 2014. Cross-domain and cross-category emotion tagging for comments of online news. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, 627--636. Google ScholarDigital Library
- L. Zhuang, F. Jing, and X. Zhu. 2006. Movie review mining and summarization. In Proceedings of the ACM International Conference on Information and Knowledge Management. 43--50. Google ScholarDigital Library
Index Terms
- Latent Discriminative Models for Social Emotion Detection with Emotional Dependency
Recommendations
Sentiment topic models for social emotion mining
The rapid development of social media services has facilitated the communication of opinions through online news, blogs, microblogs/tweets, instant-messages, and so forth. This article concentrates on the mining of readers' emotions evoked by social ...
Sentic Computing for social media marketing
In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers ...
Building emotional dictionary for sentiment analysis of online news
Sentiment analysis of online documents such as news articles, blogs and microblogs has received increasing attention in recent years. In this article, we propose an efficient algorithm and three pruning strategies to automatically build a word-level ...
Comments