Aspect-Opinion Correlation Aware and Knowledge-Expansion Few Shot Cross-Domain Sentiment Classification

• Published in: IEEE transactions on affective computing Vol. 13; no. 4; pp. 1691 - 1704
• Main Authors: Ren, Haopeng, Cai, Yi, Zeng, Yushi, Ye, Jinghui, Leung, Ho-fung, Li, Qing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Cross domain sentiment analysis; Data mining; Feature extraction; few-shot learning; Knowledge engineering; knowledge graph; Semantics; Sentiment analysis; Syntactics; Task analysis; Training
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2022.3205358

Cross-domain sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, most of the existing cross-domain sentiment classification models neglect the domain-specific features, which limits their performance especially when the domain discrepancy becomes larger. Meanwhile, the relations between the aspect and opinion terms cannot be effectively modeled and thus the sentiment transfer error problem is suffered in the existing unsupervised domain-adaptation methods. To address these two issues, we propose an aspect-opinion correlation aware and knowledge-expansion few shot cross-domain sentiment classification model. Sentiment classification can be effectively conducted with only a few support instances of the target domain. Extensive experiments are conducted and the experimental results show the effectiveness of our proposed model.