Jia Xibin, Zeng Meng, Mi Qing, Hu Yongli. Domain Alignment Adversarial Unsupervised Cross-Domain Text Sentiment Analysis Algorithm[J]. Journal of Computer Research and Development, 2022, 59(6): 1255-1270. DOI: 10.7544/issn1000-1239.20210039
Citation:
Jia Xibin, Zeng Meng, Mi Qing, Hu Yongli. Domain Alignment Adversarial Unsupervised Cross-Domain Text Sentiment Analysis Algorithm[J]. Journal of Computer Research and Development, 2022, 59(6): 1255-1270. DOI: 10.7544/issn1000-1239.20210039
Jia Xibin, Zeng Meng, Mi Qing, Hu Yongli. Domain Alignment Adversarial Unsupervised Cross-Domain Text Sentiment Analysis Algorithm[J]. Journal of Computer Research and Development, 2022, 59(6): 1255-1270. DOI: 10.7544/issn1000-1239.20210039
Citation:
Jia Xibin, Zeng Meng, Mi Qing, Hu Yongli. Domain Alignment Adversarial Unsupervised Cross-Domain Text Sentiment Analysis Algorithm[J]. Journal of Computer Research and Development, 2022, 59(6): 1255-1270. DOI: 10.7544/issn1000-1239.20210039
1(Faculty of Information Technology, Beijing University of Technology, Beijing 100124)
2(Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology(Beijing University of Technology), Beijing 100124) 3(Beijing Institute of Artificial Intelligence(Beijing University of Technology), Beijing 100124)
Funds: This work was supported by Beijing Natural Science Foundation (4202004) and the National Natural Science Foundation of China (U19B2039, 61871276).
Sentiment analysis technique can help make effective decisions and solutions by automatically discriminating the sentiment polarity in a practical application scene. However, it requires a large amount of annotated samples. To reduce the dependence on manual annotation, some researchers propose the domain adaptation based cross-domain sentiment analysis methods, which transfer a source domain model trained on an adequately labeled dataset to an unlabeled target domain. However, existing domain adaptation methods transfer from only one angle, namely, reducing the discrepancy of domain-specific features or simply extracting the domain-invariant features. To make use of domain-specific features and domain-invariant features together, we propose an unsupervised domain adaptation sentiment analysis algorithm in this paper for unsupervised cross-domain sentiment classification tasks. The algorithm reduces the domain discrepancy on different semantic layers with a progressive transfer strategy, and adopts the synergistic optimization of domain adaptation algorithm in high-level semantic subspace to transfer the domain knowledge of cross-domain text data. We validate our algorithm on 2 public review datasets with 24 cross-domain sentiment classification tasks. It is compared with 4 types of domain adaptation algorithms. The results show that our algorithm achieves the highest average accuracy. Moreover, it has better performance than the existing unsupervised cross-domain text sentiment classification algorithms in terms of the performance of classification and transferring.