Abstract
In recent years, many researchers have recognized the importance of associating events with sentiments. Previous approaches focus on generalizing events and extracting sentimental information from a large-scale corpus. However, since context is absent and sentiment is often implicit in the event, these methods are limited in comprehending the semantics of the event and capturing effective sentimental clues. In this work, we propose a novel Multi-perspective Knowledge-injected Interaction Network (MKIN) to fully understand the event and accurately predict its sentiment by injecting multi-perspective knowledge. Specifically, we leverage contexts to provide sufficient semantic information and perform context modeling to capture the semantic relationships between events and contexts. Moreover, we also introduce human emotional feedback and sentiment-related concepts to provide explicit sentimental clues from the perspective of human emotional state and word meaning, filling the reasoning gap in the sentiment prediction process. Experimental results on the gold standard dataset show that our model achieves better performance over the baseline models.
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Acknowledgements
We thank the anonymous reviewers for their insightful comments and suggestions. This work was supported by the National Key RD Program of China via grant 2021YFF0901602 and the National Natural Science Foundation of China (NSFC) via grant 62176078.
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Yi, W., Zhao, Y., Yuan, J., Zhao, W., Qin, B. (2023). Improving Affective Event Classification with Multi-perspective Knowledge Injection. In: Sun, M., et al. Chinese Computational Linguistics. CCL 2023. Lecture Notes in Computer Science(), vol 14232. Springer, Singapore. https://doi.org/10.1007/978-981-99-6207-5_25
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