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Prompted and integrated textual information enhancing aspect-based sentiment analysis

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Abstract

Aspect-based Sentiment Analysis (ABSA) aims to automatically predict the sentiment polarity of the written text based on the analysis of specific aspects. By applying various pre-trained language encoders, recent studies have achieved great success in modeling aspect and context features and measuring the word-level correlations. However, the pre-trained language models (PLM) were usually employed as the feature representations generator without any task-oriented guidance. And the syntax dependency tree is also not fully utilized. Besides, simply concatenating usually fails to exploit deep semantic features from multi-source spaces and weakens the representation of context features. In this study, we propose a novel model, namely PRoGCN (Prompted RoBERTa & Graph Convolution Network), which directly tells RoBERTa the goal of the present task by inserting the task-oriented specific prompting word to the raw text. Moreover, the prompted feature representation is also utilized to help generate textual knowledge graph, and strongly enhances the syntactic feature representation. In addition, we first introduce cross attention into our study to integrate semantic representation and syntactic representation, which has been proven to be successful in implementing and fusing multi-source information. Experimental results on five publicly available ABSA datasets validate the effectiveness of our method, and the proposed method achieves state-of-the-art performance on mentioned ABSA benchmarks.

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Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant 62176084 and Grant 62176083, and in part by the Fundamental Research Funds for the Central Universities of China under Grant PA2022GDSK0066 and Grant PA2022GDSK0068.

Funding

This research was funded by the National Natural Science Foundation of China under Grant 62176084 and Grant 62176083, and in part by the Fundamental Research Funds for the Central Universities of China under Grant PA2022GDSK0066 and Grant PA2022GDSK0068.

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Danxia Road, 230601, Hefei, Anhui, China

    Xuefeng Shi, Min Hu, Piao Shi & Yiming Tang

  2. Anhui Province Key Laboratory of Affective, Hefei University of Technology, Danxia Road, 230601, Hefei, Anhui, China

    Xuefeng Shi, Min Hu, Piao Shi & Yiming Tang

  3. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Xiyuan Avenue, 611731, Chengdu, Sichuan, China

    Fuji Ren

  4. Department of Computer Science and Technology, Tsinghua University, Hai Dian, 100084, Beijing, Beijing, China

    Jiawen Deng

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  1. Xuefeng Shi
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  2. Min Hu
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  3. Fuji Ren
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  4. Piao Shi
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Contributions

Xuefeng Shi and Fuji Ren prepared the whole plan and conducted the related experiments. Xuefeng Shi, Piao Shi and Min Hu wrote the main manuscript text, and Xuefeng Shi and Jiawen Deng prepared figures, and Xuefeng Shi and Yiming Tang prepared tables. All authors reviewed the manuscript.

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Correspondence to Fuji Ren.

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Shi, X., Hu, M., Ren, F. et al. Prompted and integrated textual information enhancing aspect-based sentiment analysis. J Intell Inf Syst 62, 91–115 (2024). https://doi.org/10.1007/s10844-023-00805-0

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