Abstract
A set-similarity joins based semi-supervised approach is presented to mine Chinese sentiment words and sentences. The set-similarity joins is taken to join nodes in unconnected sub-graphs conducted by cutting the flow graph with Ford-Fulkerson algorithm into positive and negative sets to correct wrong polarities predicted by min-cut based semi-supervised methods. Experimental results in digital, entertainment, and finance domains demonstrate the effectiveness of our proposed approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rao, D., Ravichandran, D.: Semi-supervised Polarity Lexicon Induction. In: The 12th Conference of the European Chapter of the Association For Computational Linguistics, pp. 675–682 (2009)
Dong, X., Zou, Q., Guan, Y., Gao, X., Yan, M.: Positive And Negative Polarity Analysis on Chinese Words And Sentences Based on Maximum Entropy Model And Min-Cut Model. In: The 3rd Chinese Opinion Analysis Evaluation, pp. 97–105 (2011)
Wu, Y., Wen, M.: Disambiguating Dynamic Sentiment Ambiguous Adjectives. In: The 23rd International Conference on Computational Linguistics, pp. 1191–1199 (2010)
He, H., Li, S., Xiao, F., Xu, W., Guo, J.: PRIS Sentiment Analysis System Techical Report. In: The 1st Chinese Opinion Analysis Evaluation, pp. 46–55 (2008)
Ford, L., Fulkerson, D.: Maximal Flow Through a Network. Canadian Journal of Mathematics 8, 399–404 (1954)
Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms. The MIT Press and McGraw-Hill Book Company (2001)
Das, D., Bandyopadhyay, S.: Word to Sentence Level Emotion Tagging For Bengali Blogs. In: The 47th Annual Meeting of The Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, pp. 149–152 (2009)
Blum, A., Lafferty, J., Rwebangira, M., Reddy, R.: Semi-supervised Learning Using Randomized Mincuts. In: The 21st International Conference on Machine Learning, pp. 97–104 (2004)
Zhu, X., Ghahramani, Z.: Learning From Labeled And Unlabeled Data With Label Propagation. Technical report CMU-CALD-02-107. Carnegie Mellon University (2002)
Fu, G., Wang, X.: Chinese Sentence-level Sentiment Classification Based on Fuzzy Sets. In: The 23rd International Conference on Computational Linguistics, pp. 312–319 (2010)
Meena, A., Prabhakar, T.V.: Sentence Level Sentiment Analysis in the Presence of Conjuncts Using Linguistic Analysis. In: Amati, G., Carpineto, C., Romano, G. (eds.) ECiR 2007. LNCS, vol. 4425, pp. 573–580. Springer, Heidelberg (2007)
Davidov, D., Tsur, O., Rappoport, A.: Enhanced Sentiment Learning Using Twitter Hashtags And Smileys. In: The 23rd International Conference on Computational Linguistics, pp. 241–249 (2010)
Guo, H., Zhu, H., Guo, Z., Su, Z.: Domain Customization For Aspect-oriented Opinion Analysis With Multi-level Latent Sentiment Clues. In: The 20th ACM International Conference on Information And Knowledge Management, pp. 2493–2496 (2011)
Socher, R., Pennington, J., Huang, E., Ng, A., Manning, C.: Semi-supervised Recursive Autoencoders For Predicting Sentiment Distributions. In: The 16th Conference on Empirical Methods in Natural Language Processing, pp. 151–161 (2011)
Chaudhuri, S., Ganjam, K., Ganti, V., Motwani, R.: Robust And Efficient Fuzzy Match For Online Data Cleaning. In: The 22nd ACM SIGMOD International Conference on Management of Data, pp. 313–324 (2003)
Arasu, A., Ganti, V., Kaushik, R.: Efficient Exact Set-similarity Joins. In: The 32nd International Conference on Very Large Data Bases, pp. 918–929 (2006)
Chandel, A., Hassanzadeh, O., Koudas, N., Sadoghi, M., Srivastava, D.: Benchmarking Declarative Approximate Selection Predicates. In: The 26th ACM SIGMOD International Conference on Management of Data, pp. 353–364 (2007)
Xu, R., Wang, Y., Xu, J., Zhang, Y., Zheng, H., Gui, L., Ye, L.: Chinese Opinion Analysis Based on Multi Knowledge Integration And Multi Classifier Voting. In: The 3rd Chinese Opinion Analysis Evaluation, pp. 77–87 (2011)
Xu, H., Sun, L., Yao, T., Liao, X.: Overview of The Third Chinese Opinion Analysis Evaluation. In: The 3rd Chinese Opinion Analysis Evaluation, pp. 1–24 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dong, X., Zou, Q., Guan, Y. (2012). Set-Similarity Joins Based Semi-supervised Sentiment Analysis. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-34475-6_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34474-9
Online ISBN: 978-3-642-34475-6
eBook Packages: Computer ScienceComputer Science (R0)