Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement

doi:10.3969/j.issn.1006-2475.2023.10.001

Abstract

Abstract: Aspect based sentiment analysis can accurately determine the emotional polarity of aspect words in sentences， and plays an important role in social networking， e-commerce and other fields. Most of the existing methods model the relationship between context and target words through sequence representation or attention mechanism， but ignore the background knowledge of text and the conceptual links between aspect words， resulting in insufficient semantic relationships learned. To solve the above problems， the Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement （ABSA-KE） is proposed. First， the features are extracted and the corresponding word vector is obtained through the pre-training model BERT， and the dependency tree corresponding to the text is obtained using the parser. Then， the joint modeling of BiLSTM and graph attention network is used to learn the node embedded representation and obtain the text vector. Second， the external knowledge base is used to introduce the aspect word knowledge vector in different contexts to enhance the aspect level emotion analysis model， and finally the emotion classification task is carried out. Compared with the existing models， the experimental results show that the proposed model is effective and reasonable in aspect level emotion analysis tasks.

Key words: , aspect based sentiment analysis； graph attention network； external knowledge base； BERT； dependency tree

CLC Number:

TP391

LI Shi-yue, MENG Jia-na, YU Yu-hai, LI Xue-ying, XU Ying-ao. Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement[J]. Computer and Modernization, 2023, 0(10): 1-8.

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