Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (1): 1-7.doi: 10.6040/j.issn.1672-3961.0.2019.293

• Machine Learning & Data Mining •     Next Articles

Cross-domain text sentiment classification based on domain-adversarialnetwork and BERT

Guoyong CAI(),Qiang LIN,Kaiqi REN   

  1. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, Guangxi, China
  • Received:2019-06-10 Online:2020-02-20 Published:2020-02-14
  • Supported by:
    国家自然科学基金资助项目(61763007);广西自然科学基金重点资助项目(2017JJD160017)

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Abstract:

In order to capture more sentence semantic information from the extracted shared sentiment features for cross-domain sentiment analysis, a deep network model based on domain adversarial mechanism and BERT (bidirectional encoder representations from transformers) was proposed. The model firstly used BERT to obtain the semantic representation vectors of sentences, and then extracted the local features of sentences with a convolutional neural network. A domain adversarial neural network was designed to make the representations of features extracted from different domains to be as indistinguishable as possible, that was, the features extracted from source domain and target domain had much more similarities; and a sentiment classifier was trained on the source domain dataset with sentiment labels, and it was expected that the trained sentiment classifier would have good classification performance in the source domain, and in the target domain. The experimental results on Amazon product reviews dataset showed that the proposed method achieved the expectation and was competent for achieving cross-domain text sentiment classification.

Key words: cross-domain, sentiment analysis, convolution neural network, domain adversarial network, shared sentiment features

CLC Number: 

  • TP391

Fig.1

Model BCN on cross-domain text sentiment analysis"

Fig.2

Transformer structure"

Fig.3

Multi-Head Attention"

Fig.4

Convolution for text"

Table 1

Statistical information of datasets  条"

领域 积极评论 消极评论 无标签评论
Book 3 000 3 000 9 750
dvd 3 000 3 000 11 843
electronics 3 000 3 000 17 009
Kitchen 3 000 3 000 13 856
Video 3 000 3 000 30 180

Table 2

Accuracy performance of cross-domain sentiment analysis on Amazon product review data set"

源领域 目标领域 方法
S-only DANN mSDA AMN HATN BD BCN
Books DVD 0.805 7 0.834 2 0.861 2 0.856 2 0.870 7 0.893 8 0.893 3
Kitchen 0.716 3 0.779 0 0.810 5 0.818 8 0.870 3 0.917 0 0.919 0
Electronics 0.736 5 0.762 7 0.790 2 0.805 5 0.857 5 0.913 5 0.914 0
Video 0.814 5 0.832 3 0.849 8 0.872 5 0.878 0 0.896 3 0.901 7
DVD Books 0.764 5 0.807 7 0.851 7 0.845 3 0.877 8 0.905 0 0.910 5
Kitchen 0.734 3 0.781 5 0.826 0 0.816 7 0.874 7 0.915 5 0.922 3
Electronics 0.731 2 0.763 5 0.761 7 0.804 2 0.863 2 0.915 0 0.914 7
Video 0.827 5 0.859 5 0.838 0 0.874 0 0.891 2 0.910 3 0.917 1
Video DVD 0.824 3 0.841 5 0.859 0 0.868 8 0.879 0 0.905 0 0.909 1
Kitchen 0.713 3 0.752 2 0.795 2 0.809 0 0.864 5 0.915 3 0.917 1
Electronics 0.718 7 0.757 2 0.776 7 0.796 8 0.859 8 0.8985 0.900 0
Books 0.770 3 0.800 3 0.830 0 0.835 0 0.871 0 0.909 1 0.907 6
Electronics DVD 0.726 0 0.762 7 0.826 3 0.805 3 0.843 2 0.866 0 0.868 3
Kitchen 0.846 3 0.845 3 0.858 0 0.878 3 0.900 8 0.939 3 0.941 7
Video 0.724 8 0.772 0 0.817 0 0.821 2 0.841 8 0.864 5 0.870 0
Books 0.688 7 0.735 3 0.799 2 0.775 2 0.840 3 0.887 6 0.882 1
Kitchen DVD 0.733 2 0.753 2 0.821 8 0.795 0 0.847 2 0.867 2 0.870 8
Video 0.760 8 0.763 7 0.814 7 0.821 5 0.848 5 0.872 8 0.874 5
Electronics 0.831 5 0.855 3 0.880 0 0.866 8 0.893 3 0.933 8 0.930 8
Books 0.715 3 0.741 7 0.805 5 0.790 5 0.848 8 0.888 8 0.882 3
平均准确率 0.759 2 0.790 0 0.823 6 0.827 9 0.866 1 0.900 7 0.902 3

Fig.5

Accuracy of different convolution kernel size"

Fig.6

Accuracy of different β values"

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