The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.09 - Sept. (2012 vol.24)
pp: 1658-1670
Shenghua Bao , IBM Research-China, Beijing
Shengliang Xu , Shanghai Jiao Tong University, Shanghai
Li Zhang , IBM Research-China, Beijing
Rong Yan , Facebook, Palo Alto
Zhong Su , IBM China-Research, Beijing
Dingyi Han , Shanghai Jiao Tong University, Shanghai
Yong Yu , Shanghai Jiao Tong University, Shanghai
ABSTRACT
This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, we propose a joint emotion-topic model by augmenting Latent Dirichlet Allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.
INDEX TERMS
Text mining, Predictive models, Joints, Context modeling, Data models, Blogs, Software, performance evaluation, Affective text mining, emotion-topic model
CITATION
Shenghua Bao, Shengliang Xu, Li Zhang, Rong Yan, Zhong Su, Dingyi Han, Yong Yu, "Mining Social Emotions from Affective Text", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 9, pp. 1658-1670, Sept. 2012, doi:10.1109/TKDE.2011.188
REFERENCES
[1] R. Cai, C. Zhang, C. Wang, L. Zhang, and W.-Y. Ma., "Musicsense: Contextual Music Recommendation Using Emotional Allocation," Proc. 15th Int'l Conf. Multimedia, pp. 553-556, 2007.
[2] C. Strapparava and R. Mihalcea, "Semeval-2007 Task 14: Affective Text," Proc. Fourth Int'l Workshop Semantic Evaluations (SemEval '07), pp. 70-74, 2007.
[3] C. Yang, K.H.-Y. Lin, and H.-H. Chen, "Emotion Classification Using Web Blog Corpora," Proc. IEEE/WIC/ACM Int'l Conf. Web Intelligence (WI '07), pp. 275-278, 2007.
[4] C.O. Alm, D. Roth, and R. Sproat, "Emotions from Text: Machine Learning for Text-Based Emotion Prediction," Proc. Joint Conf. Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP '05), pp. 579-586, 2005.
[5] C. Strapparava and R. Mihalcea, "Learning to Identify Emotions in Text," Proc. 23rd Ann. ACM Symp. Applied Computing (SAC '08), pp. 1556-1560, 2008.
[6] A. Esuli and F. Sebastiani, "Sentiwordnet: A Pub-Licly Available Lexical Resource for Opinion Mining," Proc. Fifth Int'l Conf. Language Resources and Evaluation (LREC '06), 2006.
[7] C. Strapparava and A. Valitutti, "Wordnet-Affect: An Affective Extension of Wordnet," Proc. Fourth Int'l Conf. Language Resources and Evaluation (LREC '04), 2004.
[8] D.M. Blei, A.Y. Ng, and M.I. Jordan, "Latent Dirichlet Allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[9] C.P. Robert and G. Casella, Monte Carlo Statistical Methods, second ed. Springer Publisher 2005.
[10] S. Morinaga, K. Yamanishi, K. Tateishi, and T. Fuku-shinna, "Mining Product Reputations on the Web," Proc. Eighth ACM SIGKDDInt'l Conf. Knowledge Discovery and Data Mining (SIGKDD '02), pp. 341-349, 2002.
[11] M. Hu and B. Liu, "Mining and Summarizing Customer Reviews," Proc. 10th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD '04), pp. 168-177, 2004.
[12] A.-M. Popescu and O. Etzioni, "Extracting Product Features and Opinions from Reviews," Proc. Joint Conf. Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP '05), pp. 339-346, 2005.
[13] B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs Up? Sentiment Classification Using Machine Learning Techniques," Proc. Conf. Empirical Methods in Natural Language Processing (EMNLP '02), pp. 79-96, 2002.
[14] R. Tokuhisa, K. Inui, and Y. Matsumoto, "Emotion Classification Using Massive Examples Extracted From The Web," Proc. 22nd Int'l Conf. Computational Linguistics (Coling '08), pp. 881-888, 2008.
[15] H. Liu, H. Lieberman, and T. Selker, "A model of Textual Affect Sensing Using Real-World Knowledge," Proc. Int'l Conf. Intelligent User Interfaces (IUI '03), 2003.
[16] A.J. Gill, D. Gergle, R.M. French, and J. Oberlander, "Emotion Rating from Short Blog Texts," Proc. 26th Ann. SIGCHI Conf. Human Factors in Computing Systems (CHI '08), pp. 1121-1124, 2008.
[17] G. Mishne, K. Balog, M. de Rijke, and B. Ernsting, "Moodviews: Tracking and Searching Mood-Annotated Blog Posts," Proc. Int'l AAAI Conf. Weblogs and Social Media (ICWSM '07), 2007.
[18] K. Balog and M. de Rijke, "How to Overcome Tiredness: Estimating Topic-Mood Associations," Proc. Int'l AAAI Conf. Weblogs and Social Media (ICWSM '07), 2007.
[19] K. Balog, G. Mishne, and M. Rijke, "Why Are They Excited? Identifying and Explaining Spikes in Blog Mood Levels," Proc. Ninth Conf. European Chapter of the Assoc. for Computational Linguistics (EACL '06), 2006.
[20] G. Mishne and M. de Rijke, "Capturing Global Mood Levels Using Blog Posts," Proc. AAAI Spring Symp. Computational Approaches to Analysing Weblogs (AAAI-CAAW '06), 2006.
[21] H. Liu, T. Selker, and H. Lieberman, "Visualizing the Affective Structure of a Text Document," Proc. CHI '03 Extended Abstracts on Human Factors in Computing Systems Conf., 2003.
[22] T. Hofmann, "Probabilistic Latent Semantic Indexing," Proc. 22nd Ann. Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR '99), 1999.
[23] M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth, "The Author-Topic Model for Authors and Documents," Proc. 20th Conf. Uncertainty in Artificial Intelligence (UAI '04), pp. 487-494, 2004.
[24] I. Titov and R. McDonald, "A Joint Model of Text and Aspect Ratings for Sentiment Summarization," Proc. 46th Ann. Meeting of the Assoc. for Computational Linguistics (ACL '08), June 2008.
[25] X. Wang and A. McCallum, "Topic over Time: A Non-Markov Continuous-Time Model of Topical Trends," Proc. 12th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (SIGKDD '06), pp. 424-433, 2006.
[26] Q. Mei, X. Ling, M. Wondra, H. Su, and C. Zhai, "Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs," Proc. 16th Int'l World Wide Web Conf. (WWW '07), 2007.
[27] W.-H. Lin, E. Xing, and A. Hauptmann, "A Joint Topic and Perspective Model for Ideological Discourse," Proc. European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD '08), pp. 17-32, 2008.
[28] T. Griffiths and M. Steyvers, "Finding Scientific Topics," Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 5228-5235, 2004.
[29] P.-C. Chang, M. Galley, and C. Manning, "Optimizing Chinese Word Segmentation for Machine Translation Performance," Proc. Assoc. for Computational Linguistics (ACL) Third Workshop Statistical Machine Translation, 2008.
[30] J. Guo, S. Xu, S. Bao, and Y. Yu, "Tapping on the Potential of q&A Community by Recommending Answer Providers," Proc. ACM 17th Conf. Information and Knowledge Management (CIKM '08), 2008.
[31] E. Erosheva, S. Fienberg, and J. Lafferty, "Mixed-Membership Models of Scientific Publications," Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 5220-5227, 2004.
[32] R.M. Nallapati, A. Ahmed, E.P. Xing, and W.W. Cohen, "Joint Latent Topic Models for Text and Citations," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD '08), pp. 542-550, 2008.
9 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool