Abstract
Social emotion classification is to predict the distribution of readers’ emotions evoked by a document (e.g., news article). Previous work has shown that both semantic and topical information can help improve classification performance. However, many existing topic-based neural models represent the topical feature of document with only topic probabilities, ignoring the fine-grained semantic feature of terms in each topic. Moreover, traditional RNN-based semantic networks often face the disadvantage of slow training. In this paper, we propose a hybrid semantic-topic co-encoding network. It contains a semantics-driven topic encoder to compose topic embeddings, and also utilizes a forward self-attention network to exploit document semantics. Finally, the semantic and topical features of the document are adaptively integrated through a gate layer, which generates the document representation for social emotion classification. Experimental results on three public datasets show that the proposed model outperforms the state-of-the-art approaches in terms of higher accuracy and average Pearson correlation coefficient. Moreover, the proposed model runs fast and with better explainability.
Supported by National Natural Science Foundation of China (Grant No: 62172167).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For example, the representative terms may be related to “sadness” in some topics, and may be related to “happiness” in some other topics.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Bao, S., et al.: Mining social emotions from affective text. IEEE Trans. Knowl. Data Eng. 24(9), 1658–1670 (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Cheng, X., Yan, X., Lan, Y., Guo, J.: BTM: topic modeling over short texts. IEEE Trans. Knowl. Data Eng. 26(12), 2928–2941 (2014)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)
Katz, P., Singleton, M., Wicentowski, R.: SWAT-MP: the semeval-2007 systems for task 5 and task 14. In: Proceedings of the 4th International Workshop On Semantic Evaluations, pp. 308–313. Association for Computational Linguistics (2007)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lai, Y., Zhang, L., Han, D., Zhou, R., Wang, G.: Fine-grained emotion classification of Chinese microblogs based on graph convolution networks. World Wide Web 23(5), 2771–2787 (2020). https://doi.org/10.1007/s11280-020-00803-0
Li, X., Rao, Y., Xie, H., Lau, R.Y.K., Yin, J., Wang, F.L.: Bootstrapping social emotion classification with semantically rich hybrid neural networks. IEEE Trans. Affect. Comput. 8(4), 428–442 (2017)
Li, X., Rao, Y., Xie, H., Liu, X., Wong, T.L., Wang, F.L.: Social emotion classification based on noise-aware training. Data Knowl. Eng. 123, 101605 (2017)
Li, X., et al.: Weighted multi-label classification model for sentiment analysis of online news. In: 2016 International Conference on Big Data and Smart Computing (BigComp), pp. 215–222. IEEE (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26, pp. 3111–3119 (2013)
Rao, Y.: Contextual sentiment topic model for adaptive social emotion classification. IEEE Intell. Syst. 1, 41–47 (2016)
Rao, Y., Li, Q., Wenyin, L., Wu, Q., Quan, X.: Affective topic model for social emotion detection. Neural Netw. 58, 29–37 (2014)
Scherer, K.R., Wallbott, H.G.: Evidence for universality and cultural variation of differential emotion response patterning. J. Pers. Soc. Psychol. 66(2), 310 (1994)
Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., Zhang, C.: Disan: directional self-attention network for RNN/CNN-free language understanding. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: affective text. In: Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval-2007), pp. 70–74 (2007)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems 30, pp. 5998–6008 (2017)
Wang, C., Wang, B., Xiang, W., Xu, M.: Encoding syntactic dependency and topical information for social emotion classification. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 881–884. ACM (2019)
Yang, X., Wang, B.: Local matrix approximation based on graph random walk. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1037–1040 (2019)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480–1489 (2016)
Zhao, X., Wang, C., Yang, Z., Zhang, Y., Yuan, X.: Online news emotion prediction with bidirectional LSTM. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds.) WAIM 2016. LNCS, vol. 9659, pp. 238–250. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39958-4_19
Zhou, P., et al.: Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 207–212 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, L., Wang, B., Xiang, W., Xu, M., Xu, H. (2022). A Hybrid Semantic-Topic Co-encoding Network for Social Emotion Classification. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-05933-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-05932-2
Online ISBN: 978-3-031-05933-9
eBook Packages: Computer ScienceComputer Science (R0)