Abstract
In recent years, sentiment analysis has become a hot topic in natural language processing. Although sentiment analysis research in English is rather mature, Chinese sentiment analysis has just set sail, as the limited amount of sentiment resources in Chinese severely limits its development. In this paper, we present a method for the construction of a Chinese sentiment resource. We utilize both English sentiment resources and the Chinese knowledge base NTU Multi-lingual Corpus. In particular, we first propose a resource based on SentiWordNet and a second version based on SenticNet.
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NLP&CC is an annual conference of Chinese information technology professional committee organized by Chinese computer Federation (CCF). More details are available at http://tcci.ccf.org.cn/conference/2013/index.html.
References
Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)
Baldwin, T., Kim, S., Bond, F., Fujita, S., Martinez, D., Tanaka, T.: A reexamination of MRD-based word sense disambiguation. ACM Trans. Asian Lang. Inf. Process. (TALIP) 9(1), 4 (2010)
Cambria, E., Hussain, A.: Sentic Computing: A Common-Sense-Based Framework for Concept-Level Sentiment Analysis. Springer, Cham (2015)
Cambria, E., Poria, S., Gelbukh, A., Thelwall, M.: Sentiment analysis is a big suitcase. IEEE Intell. Syst. 32(6), 74–80 (2017)
Cambria, E., Poria, S., Hazarika, D., Kwok, K.: SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: AAAI, pp. 1795–1802 (2018)
Cambria, E., Hussain, A., Havasi, C., Eckl, C.: Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) COST 2102 Int. Training School 2009. LNCS, vol. 5967, pp. 148–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12397-9_12
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr, E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI, vol. 5, p. 3 (2010)
Chaturvedi, I., Cambria, E., Vilares, D.: Lyapunov filtering of objectivity for Spanish sentiment model. In: IJCNN, pp. 4474–4481. Vancouver (2016)
Chen, Q., Li, W., Lei, Y., Liu, X., He, Y.: Learning to adapt credible knowledge in cross-lingual sentiment analysis. In: ACL (2015)
Dong, Z., Dong, Q.: HowNet and the Computation of Meaning. World Scientific (2006)
Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)
Gui, L., et al.: Cross-lingual opinion analysis via negative transfer detection. In: ACL, vol. 2, pp. 860–865 (2014)
Jain, S., Batra, S.: Cross-lingual sentiment analysis using modified brae. In: EMNLP. Association for Computational Linguistics, pp. 159–168 (2015)
Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, vol. 100107 (2006)
Lambert, P.: Aspect-level cross-lingual sentiment classification with constrained SMT. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers). Association for Computational Linguistics, pp. 781–787 (2015)
Lesk, M.: Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone. In: Proceedings of the 5th Annual International Conference on Systems Documentation, pp. 24–26. ACM (1986)
Li, C., et al.: Recursive deep learning for sentiment analysis over social data. In: Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02. IEEE Computer Society, pp. 180–185 (2014)
Ma, Y., Cambria, E., Gao, S.: Label embedding for zero-shot fine-grained named entity typing. In: COLING, pp. 171–180. Osaka (2016)
Majumder, N., Poria, S., Gelbukh, A., Cambria, E.: Deep learning based document modeling for personality detection from text. IEEE Intell. Syst. 32(2), 74–79 (2017)
McArthur, T., McArthur, F.: The Oxford Companion to the English Language. Oxford Companions Series. Oxford University Press, Oxford (1992)
Mihalcea, R., Banea, C., Wiebe, J.M.: Learning multilingual subjective language via cross-lingual projections (2007)
Mihalcea, R., Garimella, A.: What men say, what women hear: finding gender-specific meaning shades. IEEE Intell. Syst. 31(4), 62–67 (2016)
Pavlenko, A.: Emotions and the body in Russian and English. Pragmat. Cogn. 10(1), 207–241 (2002)
Poria, S., Cambria, E., Gelbukh, A.: Aspect extraction for opinion mining with a deep convolutional neural network. Knowl.-Based Syst. 108, 42–49 (2016)
Poria, S., Cambria, E., Hazarika, D., Vij, P.: A deeper look into sarcastic tweets using deep convolutional neural networks. In: COLING, pp. 1601–1612 (2016)
Quan, C., Ren, F.: Construction of a blog emotion corpus for Chinese emotional expression analysis. In: EMNLP. Association for Computational Linguistics, pp. 1446–1454 (2009)
Rajagopal, D., Cambria, E., Olsher, D., Kwok, K.: A graph-based approach to commonsense concept extraction and semantic similarity detection. In: WWW, Rio De Janeiro, pp. 565–570 (2013)
Tan, L., Bond, F.: Building and annotating the linguistically diverse NTU-MC (NTU-multilingual corpus). Int. J. Asian Lang. Proc. 22(4), 161–174 (2012)
Wierzbicka, A.: Preface: bilingual lives, bilingual experience. J. Multiling. Multicult. Develop. 25(2–3), 94–104 (2004)
Wu, H.H., Tsai, A.C.R., Tsai, R.T.H., Hsu, J.Y.: Building a graded Chinese sentiment dictionary based on commonsense knowledge for sentiment analysis of song lyrics. J. Inf. Sci. Eng. 29(4), 647–662 (2013)
Zhao, Y., Qin, B., Liu, T.: Creating a fine-grained corpus for Chinese sentiment analysis. IEEE Intell. Syst. 30(1), 36–43 (2015)
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Peng, H., Cambria, E. (2018). CSenticNet: A Concept-Level Resource for Sentiment Analysis in Chinese Language. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_7
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