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Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis

Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis

Weihao Huang, Shaohua Cai, Haoran Li, Qianhua Cai
Copyright: © 2023 |Volume: 19 |Issue: 1 |Pages: 20
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781668479025|DOI: 10.4018/IJDWM.321107
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MLA

Huang, Weihao, et al. "Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis." IJDWM vol.19, no.1 2023: pp.1-20. http://doi.org/10.4018/IJDWM.321107

APA

Huang, W., Cai, S., Li, H., & Cai, Q. (2023). Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis. International Journal of Data Warehousing and Mining (IJDWM), 19(1), 1-20. http://doi.org/10.4018/IJDWM.321107

Chicago

Huang, Weihao, et al. "Structure Graph Refined Information Propagate Network for Aspect-Based Sentiment Analysis," International Journal of Data Warehousing and Mining (IJDWM) 19, no.1: 1-20. http://doi.org/10.4018/IJDWM.321107

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Abstract

The main task of aspect-based sentiment analysis is to determine the sentiment polarity of a given aspect in the sentence. A major issue lies in identifying the aspect sentiment is to establish the relationship between the aspect and its opinion words. The application of syntactic dependency trees is one such resolution. However, the widely-used dependency parsers still have challenges in obtaining a solid sentiment classification result. In this work, an information propagation graph convolutional network based on syntactic structure optimization is proposed on the task of ABSA. To further complement the syntactic information, the semantic information is incorporated to learn the representations using graph information propagation mechanism. In addition, the effects of syntactic and semantic information are adapted via feature separation. Experimental results on three benchmark datasets show that the proposed model achieves satisfying performance against the state-of-the-art methods, indicating that the model can precisely build the relation between aspect and its context words.