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).
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Notes
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For example, the representative terms may be related to “sadness” in some topics, and may be related to “happiness” in some other topics.
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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
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