Abstract
Aspect-based sentiment analysis (ABSA) has been a hot research topic due to its ability to fully exploit people’s opinions through social media texts. Compared with analyzing sentiment in short texts, conversational aspect-based sentiment quadruple analysis, also known as DiaASQ, aiming to extract the sentiment quadruple of target-aspect-opinion-sentiment in a dialogue, is a relatively new task that involves multiple speakers with varying stances in a conversation. Conversations are longer than ordinary texts and have richer contexts, which can lead to context loss and pairing errors. To address this issue, this work proposes a context-fusion encoding method based on conversation threads and lengths to integrate the speech of different speakers, enabling the model to better understand conversational context and extract cross-utterance quadruples. Experimental results have demonstrated that the proposed method achieves an average F1-score of 42.12% in DiaASQ, which is 6.48% higher than the best comparative model, indicating superior performance.
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Acknowledgements
This work was supported by Natural Science Foundation of Guangdong Province (No. 2021A1515011864) and National Natural Science Foundation of China (No. 71472068).
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Xiao, X., Chen, J., Li, Q., Huang, P., Xu, Y. (2023). Enhancing Conversational Aspect-Based Sentiment Quadruple Analysis with Context Fusion Encoding Method. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_17
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DOI: https://doi.org/10.1007/978-3-031-44699-3_17
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