Abstract
Aspect-Category-Opinion-Sentiment quadruple extraction (ACOS) is the novel and challenging sentiment analysis task, which aims to analyze the full range of emotional causes. Existing approaches focus on solving explicit sentiment, but struggle with analyzing implicit sentiment reviews. In this paper, to address the issue, we propose SI-TS, a framework that takes implicit sentiment extraction into account. Specifically, we design target structure (TS) to capture implicit sentiment by converting sentiment elements into a structured format. Furthermore, to adaptively generate appropriate TS according to different sentiment scenarios, we design an prompt template based sentiment instructor(SI). It assists the framework in effectively extracting implicit sentiment elements from the reviews. Extensive experiments were conducted on two widely used ACOS benchmarks, and improvements in F1 values were observed. Specifically, we achieved a 1.05% and 1.28% improvement in F1 values for Laptop-ACOS and Restaurant-ACOS, respectively. Notably, significant results were achieved in extracting implicit sentiment.
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Acknowledgements
The authors would like to thank the Associate Editor and anonymous reviewers for their valuable comments and suggestions. This work is funded in part by the National Natural Science Foundation of China under Grants No.62176029. This work also is supported in part by the Chongqing Technology Innovation and Application Development Special under Grants CSTB2022TIAD-KPX0206. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor.
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Liu, Y., Zhong, J. (2023). Implicit Sentiment Extraction Using Structure Generation with Sentiment Instructor Prompt Template. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_7
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