Abstract
Sentiment analysis plays an important role in many applications. This paper introduces our ongoing work related to the sentiment analysis on Chinese conversation. The main purpose is to construct a Chinese conversation corpus for sentiment analysis and provide a benchmark result on this corpus. To explore the effectiveness of machine learning based approaches for sentiment analysis on Chinese conversation, we firstly collected conversational data from some online English learning websites and our instant messages, and manually annotated it with three sentiment polarities and 22 fine-grained emotion classes. Then we applied multiple representative classification methods to evaluate the corpus. The evaluation results provide good suggestions for the future research. And we will release the corpus with gold standards publicly for research purposes.
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References
Husin, N., Abdullah, M.T., Mahmod, R.: A systematic literature review for topic detection in chat conversation for cyber-crime investigation. Int. J. Digit. Content Technol. Appl. 8(3), 22 (2014)
Zhao, Y., Qin, B., Liu, T.: Creating a fine-grained corpus for chinese sentiment analysis. IEEE Intell. Syst. 30(1), 36–43 (2015)
Zhang, L., Chen, C.: Sentiment classification with convolutional neural networks: an experimental study on a large-scale Chinese conversation corpus. In: 12th International Conference on Computational Intelligence and Security (CIS 2016), pp. 165–169 (2016)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP 2013, vol. 1631, p. 1642 (2013)
Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd ACL, pp. 655–665 (2014)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP 2015, pp. 1422–1432 (2015)
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: EMNLP 2016, pp. 1650–1659 (2016)
Quan, C., Ren, F.: Construction of a blog emotion corpus for Chinese emotional expression analysis. In: EMNLP 2009, pp. 1446–1454 (2009)
Li, C., Xu, B., Wu, G., He, S., Tian, G., Hao, H.: Recursive deep learning for sentiment analysis over social data. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 1, pp. 180–185 (2014)
Liao, X., Li, B., Xu, L.: Overview of topic-based Chinese message polarity classification in SIGHAN 2015. In: ACL-IJCNLP 2015, p. 56 (2015)
Peng, H., Cambria, E., Hussain, A.: A review of sentiment analysis research in Chinese language. Cogn. Comput. 9(4), 1–13 (2017)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: AAAI 2015, pp. 2267–2273 (2015)
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. CoRR abs/1607.01759 (2016)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: NIPS 2015, pp. 649–657 (2015)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP 2014, pp. 1746–1751 (2014)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: NAACL HLT 2016 (2016)
Zhou, Y., Xu, J., Cao, J., Xu, B., Li, C., Xu, B.: Hybrid attention networks for Chinese short text classification. In: CICLing 2017 (2017)
Zhou, Y., Xu, B., Xu, J., Yang, L., Li, C., Xu, B.: Compositional recurrent neural networks for Chinese short text classification. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 137–144 (2016)
Acknowledgments
This work is supported by the National Natural Science Foundation (No. 61602479), National High Technology Research and Development Program of China (No. 2015AA015402) and National Key Technology R&D Program of China under No. 2015BAH53F02.
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Zhou, Y., Li, C., Xu, B., Xu, J., Yang, L., Xu, B. (2018). Constructing a Chinese Conversation Corpus for Sentiment Analysis. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2017. Lecture Notes in Computer Science(), vol 10619. Springer, Cham. https://doi.org/10.1007/978-3-319-73618-1_48
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DOI: https://doi.org/10.1007/978-3-319-73618-1_48
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