Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network

  • Xiang, Yan (Dept. of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Zhang, Jiqun (Dept. of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Zhang, Zhoubin (Dept. of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Yu, Zhengtao (Dept. of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Xian, Yantuan (Dept. of Information Engineering and Automation, Kunming University of Science and Technology)
  • Received : 2021.03.25
  • Accepted : 2022.02.09
  • Published : 2022.10.31
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Abstract

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 62162037) and the General projects of basic research in Yunnan Province (No. 202001AT070047 and 202001AT070046).

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