• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
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

Domain Alignment Adversarial Unsupervised Cross-Domain Text Sentiment Analysis Algorithm

Funds: This work was supported by Beijing Natural Science Foundation (4202004) and the National Natural Science Foundation of China (U19B2039, 61871276).
More Information
  • Published Date: May 31, 2022
  • 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.
  • Related Articles

    [1]Feng Yiming, Qian Zhen, Li Guanghui, Dai Chenglong. Synergistic Optimization Method for Adaptive Hierarchical Federated Learning in Hetero-geneous Edge Environments[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202550146
    [2]Jia Xibin, Li Chen, Wang Luo, Zhang Muchen, Liu Xiaojian, Zhang Yangyang, Wen Jiakai. A Multimodal Cross-Domain Sentiment Analysis Algorithm Based on Feature Disentanglement Meta-Optimization[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202440624
    [3]Sun Hao, Han Zhongyi, Wang Fan, Yin Yilong. Backward Pseudo-Label and Optimal Transport for Unsupervised Domain Adaptation[J]. Journal of Computer Research and Development, 2023, 60(8): 1696-1710. DOI: 10.7544/issn1000-1239.202330163
    [4]Liu Xinyi, Ning Bo, Wang Ming, Yang Chao, Shang Di, Li Guanyu. Fine-Grained Sentiment Triplet Extraction Method Based on Syntactic Enhancement[J]. Journal of Computer Research and Development, 2023, 60(7): 1649-1660. DOI: 10.7544/issn1000-1239.202220233
    [5]Zhang Dongjie, Huang Longtao, Zhang Rong, Xue Hui, Lin Junyu, Lu Yao. Fake Review Detection Based on Joint Topic and Sentiment Pre-Training Model[J]. Journal of Computer Research and Development, 2021, 58(7): 1385-1394. DOI: 10.7544/issn1000-1239.2021.20200817
    [6]Cai Guoyong, Lü Guangrui, Xu Zhi. A Hierarchical Deep Correlative Fusion Network for Sentiment Classification in Social Media[J]. Journal of Computer Research and Development, 2019, 56(6): 1312-1324. DOI: 10.7544/issn1000-1239.2019.20180341
    [7]Chen Ke, Liang Bin, Ke Wende, Xu Bo, Zeng Guochao. Chinese Micro-Blog Sentiment Analysis Based on Multi-Channels Convolutional Neural Networks[J]. Journal of Computer Research and Development, 2018, 55(5): 945-957. DOI: 10.7544/issn1000-1239.2018.20170049
    [8]Li Ran, Lin Zheng, Lin Hailun, Wang Weiping, Meng Dan. Text Emotion Analysis: A Survey[J]. Journal of Computer Research and Development, 2018, 55(1): 30-52. DOI: 10.7544/issn1000-1239.2018.20170055
    [9]Chen Long, Guan Ziyu, He Jinhong, Peng Jinye. A Survey on Sentiment Classification[J]. Journal of Computer Research and Development, 2017, 54(6): 1150-1170. DOI: 10.7544/issn1000-1239.2017.20160807
    [10]Feng Shi, Fu Yongchen, Yang Feng, Wang Daling, Zhang Yifei. Blog Sentiment Orientation Analysis Based on Dependency Parsing[J]. Journal of Computer Research and Development, 2012, 49(11): 2395-2406.
  • Cited by

    Periodical cited type(2)

    1. 郝海燕,李芳. 基于改进深度子域适应网络的图像分类方法. 沈阳理工大学学报. 2024(01): 69-74+90 .
    2. 刘可欣,张超,李文涛,牛宇鸽,卢方蕙. 面向不确定性决策分析的数据获取模型与方法综述. 人工智能科学与工程. 2024(03): 1-28 .

    Other cited types(13)

Catalog

    Article views (311) PDF downloads (236) Cited by(15)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return