Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (8): 300-307.doi: 10.11896/jsjkx.240900114

• Artificial Intelligence • Previous Articles     Next Articles

Cross-domain Aspect-based Sentiment Analysis Based on Pre-training Model with Data Augmentation

CHEN Ge, WANG Zhongqing   

  1. School of Computer Science and Technology,Soochow University,Soochow,Suzhou 215006,China
  • Received:2024-09-18 Revised:2024-12-03 Online:2025-08-15 Published:2025-08-08
  • About author:CHEN Ge, born in 2001, postgraduate, is a member of CCF（No.V5365G). Her main research interests include sentiment analysis and so on.
    WANG Zhongqing, born in 1987, Ph.D, associate professor. His main research interests include natural language processing, sentiment analysis and information extraction.

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Abstract: Aspect-based Sentiment Analysis(ABSA) is a fine-grained sentiment analysis task,which aimes at identifying specific aspects in text and exploring their sentiment orientation.To solve the problem of poor performance of ABSA model due to its in-ability to adapt to different domain language styles and lack of labeled data in target domain,this paper proposes a cross-domain aspect-based sentiment analysis method combined with pre-trained model.The pretraining model is used to generate labels for the target domain text,and the large language model is used to regenerate natural sentences with more target domain style.Finally,the generated samples and source domain samples are combined for training to predict the target domain.This experimental results on the restaurant and laptop datasets from the SemEval corpus,as well as a publicly available Web service review dataset show that,compared to existing cross-domain sentiment analysis methods,the proposed method achieves at least a 5.33% improvement in F1 score,fully demonstrating its effectiveness.

Key words: Cross-domain ABSA, Pre-training model, T5, GPT

CLC Number: 

  • TP391
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