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

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...

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Bibliographic Details
Published in:IEEE transactions on affective computing 2022-10, Vol.13 (4), p.1691-1704
Main Authors: Ren, Haopeng, Cai, Yi, Zeng, Yushi, Ye, Jinghui, Leung, Ho-fung, Li, Qing
Format: Article
Language:English
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
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Summary: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.
ISSN:1949-3045
1949-3045
DOI:10.1109/TAFFC.2022.3205358