Abstract
Recently, analyzing people’s sentiments in microblogs has attracted more and more attentions from both academic and industrial communities. The traditional methods usually treat the sentiment analysis as a kind of single-label supervised learning problem that classifies the microblog according to sentiment orientation or single-labeled emotion. However, in fact multiple fine-grained emotions may be coexisting in just one tweet or even one sentence of the microblog. In this paper, we regard the emotion detection in microblogs as a multi-label classification problem. We leverage the skip-gram language model to learn distributed word representations as input features, and utilize a Convolutional Neural Network (CNN) based method to solve multi-label emotion classification problem in the Chinese microblog sentences without any manually designed features. Extensive experiments are conducted on two public short text datasets. The experimental results demonstrate that the proposed method outperforms strong baselines by a large margin and achieves excellent performance in terms of multi-label classification metrics.
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References
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer, Heidelberg (2010)
Ye, L., Xu, R., Xu, J.: Emotion prediction of news articles from reader’s perspective based on multi-label classification. In: Proceedings of ICMLC, pp. 2019–2024 (2012)
Bhowmick, P.K.: Reader perspective emotion analysis in text through ensemble based multi-label classification framework. Comput. Inf. Sci. (CCSECIS) 2(4), 64–74 (2009)
Read, J., Pérez-Cruz, F.: Deep learning for multi-label classification. CoRR abs/1502.05988 (2015)
Liu, S., Chen, J.: A multi-label classification based approach for sentiment classification. Expert Syst. Appl. 42(3), 1083–1093 (2015)
Tang, D., Wei, F., Qin, B., Zhou, M., Liu, T.: Building large-scale Twitter-specific sentiment lexicon: a representation learning approach. In: Proceedings of COLING, pp. 172–182 (2014)
Liu, K., Li, W., Guo, M.: Emoticon smoothed language models for Twitter sentiment analysis. In: Proceedings of AAAI (2012)
Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of Tweets. CoRR abs/1308.6242 (2013)
Zhang, M., Zhou, Z.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
Zhang, M., Zhou, Z.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. (PR) 40(7), 2038–2048 (2007)
Wang, M., Liu, M., Feng, S., Wang, D., Zhang, Y.: A novel calibrated label ranking based method for multiple emotions detection in Chinese microblogs. In: Zong, C., Nie, J.-Y., Zhao, D., Feng, Y. (eds.) NLPCC 2014. CCIS, vol. 496, pp. 238–250. Springer, Heidelberg (2014)
Abdel-Hamid, O., Deng, L., Yu, D.: Exploring convolutional neural network structures and optimization techniques for speech recognition. In: Proceedings of INTERSPEECH, pp. 3366–3370 (2013)
Meng, F., Lu, Z., Wang, M., Li, H., Jiang, W., Liu, Q.: Encoding source language with convolutional neural network for machine translation. ACL (1), pp. 20–30 (2015)
Wei, Y., Xia, W., Huang, J., Ni, B., Dong, J., Zhao, Y., Yan, S.: CNN: single-label to multi-label. CoRR abs/1406.5726 (2014)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of ICML pp. 160–167 (2008)
Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING pp, 69–78 (2014)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of EMNLP pp. 1746–1751 (2014)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean. J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS (2013)
Quan, C., Ren, F.: A blog emotion corpus for emotional expression analysis in Chinese. Comput. Speech Lang. (CSL) 24(4), 726–749 (2010)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Dzeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. (PR) 45(9), 3084–3104 (2012)
Acknowledgements
The work is supported by National Natural Science Foundation of China (61370074, 61402091), the Fundamental Research Funds for the Central Universities of China under Grant N140404012.
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Wang, Y., Feng, S., Wang, D., Yu, G., Zhang, Y. (2016). Multi-label Chinese Microblog Emotion Classification via Convolutional Neural Network. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_46
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DOI: https://doi.org/10.1007/978-3-319-45814-4_46
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