Abstract
Opinion mining on microblogs is of significance because microblogging websites have attracted many users to share their experiences and express their opinions on a variety of topics. However, conventional opinion mining methods focus mainly on sentiment of texts and ignore opinion target. This paper focuses on a fine-grained opinion mining task that jointly extract opinion target and corresponding sentiment by sequence labeling. We propose a convolutional neural network (CNN)-based sequence labeling method and apply it to fine-grained opinion mining of microblogs. We empirically evaluated neural networks with different filter length and depth and analyzed the boundary of contextual feature extraction for opinion mining of microblogs. The experimental results demonstrate that the proposed CNN-based methods are better than RNN-based methods in both effectiveness and efficiency.
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References
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Kumaresan, R.: A hybrid approach for supervised twitter sentiment classification. Int. J. Comput. Sci. Bus. Inf. 7(1), 35 (2013)
Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 537–546. ACM (2013)
Cheng, J., Zhang, X., Li, P., Zhang, S., Ding, Z., Wang, H.: Exploring sentiment parsing of microblogging texts for opinion polling on Chinese public figures. Appl. Intell. 1–14 (2016)
dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING), Dublin, Ireland (2014)
Chiu, J.P.C., Nichols, E.: Named entity recognition with bidirectional lstm-cnns. arXiv preprint arXiv:1511.08308 (2015)
Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional lstm-cnns-crf. arXiv preprint arXiv:1603.01354 (2016)
Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)
Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. arXiv preprint arXiv:1412.1058 (2014)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Wu, H., Gu, Y., Sun, S., Gu, X.: Aspect-based opinion summarization with convolutional neural networks. arXiv preprint arXiv:1511.09128 (2015)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1422–1432 (2015)
Meng, F., Lu, Z., Wang, M., Li, H., Jiang, W., Liu, Q.: Encoding source language with convolutional neural network for machine translation. arXiv preprint arXiv:1503.01838 (2015)
Shen, Y., He, X., Gao, J., Deng, L., Mesnil, G.: Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, pp. 373–374. International World Wide Web Conferences Steering Committee (2014)
O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11(122–129), 1–2 (2010)
Bravo-Marquez, F., Mendoza, M., Poblete, B.: Combining strengths, emotions and polarities for boosting twitter sentiment analysis. In: Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining, p. 2. ACM (2013)
Xu, P., Sarikaya, R.: Joint intent detection and slot filling using convolutional neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (2014)
Bengio, Y., Schwenk, H., Senécal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Holmes, D.E., Jain, L.C. (eds.) Innovations in Machine Learning. Studies in Fuzziness and Soft Computing, vol. 194. Springer, Berlin (2006). doi:10.1007/3-540-33486-6_6
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. arXiv preprint arXiv:1310.4546 (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Empiricial Methods in Natural Language Processing (EMNLP 2014), vol. 12 (2014)
Lai, S., Liu, K., Xu, L., Zhao, J.: How to generate a good word embedding? arXiv preprint arXiv:1507.05523 (2015)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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The research is supported by National Natural Science Foundation of China (No. 71331008).
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Cheng, J., Li, P., Zhang, X., Ding, Z., Wang, H. (2017). CNN-Based Sequence Labeling for Fine-Grained Opinion Mining of Microblogs. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_9
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DOI: https://doi.org/10.1007/978-3-319-67274-8_9
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