Abstract
To date, the majority of sentiment analysis research has focused on English language. Recent studies, however, show that non-native English speakers heavily support the growing use of Internet. Chinese, specifically, is poised to outpace English as the dominant language online in a few years’ time. So far, just a few isolated research endeavors have been undertaken to meet the demands of real-life Chinese web environments. Natural language processing research endeavor, in fact, primarily depends on the availability of resources like lexicons and corpora, which are still very limited for sentiment analysis research in Chinese language. To this end, we are developing a Chinese common and common sense knowledge base for sentiment analysis by blending the largest existing taxonomy of English common knowledge with a semantic network of English common sense knowledge, and by using machine translation techniques to effectively translate its content into Chinese.
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References
Cambria, E., Grassi, M., Hussain, A., Havasi, C.: Sentic computing for social media marketing. Multimedia Tools and Applications (2011), http://dx.doi.org/10.1007/s11042-011-0815-0
Wu, W., Li, H., Wang, H., Zhu, K.: Towards a probabilistic taxonomy of many concepts. Technical Report MSR-TR-2011-25, MSRA (2011)
Havasi, C., Speer, R., Alonso, J.: ConceptNet 3: A flexible, multilingual semantic network for common sense knowledge. In: Proceedings of RANLP, Borovets (2007)
Cambria, E., Benson, T., Eckl, C., Hussain, A.: Sentic PROMs: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Systems with Applications (2012), http://dx.doi.org/10.1016/j.eswa.2012.02.120
Ye, Q., Lin, B., Li, Y.: Sentiment classification for Chinese reviews: A comparison between SVM and semantic approaches. In: Proceedings of the International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2341–2346 (2005)
Ye, Q., Shi, W., Li, Y.: Sentiment classification for movie reviews in Chinese by improved semantic oriented approach. In: Proceedings of HICSS (2006)
Zagibalov, T., Carroll, J.: Unsupervised classification of sentiment and objectivity in Chinese text. In: Proceedings of the International Joint Conference on Natural Language Processing, pp. 304–311 (2008)
Zagibalov, T., Carroll, J.: Automatic seed word selection for unsupervised sentiment classification of Chinese text. In: Proceedings of COLING, vol. 1, pp. 1073–1080 (2008)
Tan, S., Zhang, J.: An empirical study of sentiment analysis for Chinese documents. Expert Systems with Applications 34, 2622–2629 (2008)
Zhang, C., Zeng, D., Li, J., Wang, F., Zuo, W.: Sentiment analysis of Chinese documents: From sentence to document level. Journal of the American Society for Information Science and Technology 60, 2474–2487 (2009)
Zhai, Z., Xu, H., Li, J., Jia, P.: Sentiment classification for Chinese reviews based on key substring features. In: Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering, pp. 1–8 (2009)
Zhu, Y., Min, J., Zhou, Y., Huang, X., Wu, L.: Semantic orientation computing based on HowNet. Journal of Chinese Information Processing 20, 14–20 (2006)
Xu, L., Lin, H., Yang, Z.: Text orientation identification based on semantic comprehension. Journal of Chinese Information Processing 21, 96–100 (2007)
Xu, J., Ding, Y., Wang, X.: Sentiment classification for Chinese news using machine learning methods. Journal of Chinese Information Processing 21, 95–100 (2007)
Yao, T., Lou, D.: Research on semantic orientation analysis for topics in Chinese sentences. Journal of Chinese Information Processing 21, 73–79 (2007)
Yang, J., Hou, M., Wang, N.: Recognizing sentiment polarity in Chinese reviews based on topic sentiment sentences. In: Proceedings of NLP-KE (2010)
Dong, Z., Dong, Q.: HowNet and the Computation of Meaning. World Scientific (2000)
Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press (1998)
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: Proceedings of SIGMOD, Vancouver, 1247–1250 (2008)
Ponzetto, S., Strube, M.: Deriving a large-scale taxonomy from Wikipedia. In: AAAI, Vancouver, 1440–1445 (2007)
Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of COLING, Nantes, pp. 539–545 (1992)
Lenat, D., Guha, R.: Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley (1989)
Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. Springer, Heidelberg (2012)
Cambria, E., Song, Y., Wang, H., Hussain, A.: Isanette: A common and common sense knowledge base for opinion mining. In: Proceedings of ICDM, Vancouver (2011)
Cambria, E., Havasi, C., Hussain, A.: SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In: Proceedings of FLAIRS, Marco Island (2012)
Havasi, C., Speer, R., Pustejovsky, J., Lieberman, H.: Digital intuition: Applying common sense using dimensionality reduction. IEEE Intelligent Systems 24(4), 24–35 (2009)
Brown, P., Della Pietra, S., Della Pietra, V., Mercer, R.: The mathematics of machine translation: Parameter estimation. Computational Linguistics 2(19), 263–311 (1993)
Koehn, P., Och, F., Marcu, D.: Statistical phrase-based translation. In: Proceedings of HLT/NAACL (2003)
Zhang, J., Zhai, F., Zong, C.: Augmenting string-to-tree translation models with fuzzy use of source-side syntax. In: Proceedings of EMNLP, pp. 204–215 (2011)
Galley, M., Hopkins, M., Knight, K., Marcu, D.: What’s in a translation rule. In: Proceedings of HLT/NAACL, pp. 273–280 (2004)
Huang, Z., Cmejrek, M., Zhou, B.: Soft syntactic constraints for hierarchical phrase-based translation using latent syntactic distributions. In: Proceedings of EMNLP, pp. 138–147 (2010)
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Cambria, E., Hussain, A., Durrani, T., Zhang, J. (2012). Towards a Chinese Common and Common Sense Knowledge Base for Sentiment Analysis. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_46
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