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An attention network based on feature sequences for cross-domain sentiment classification
Intelligent Data Analysis ( IF 1.7 ) Pub Date : 2021-04-20 , DOI: 10.3233/ida-205130
Jiana Meng , Yu Dong , Yingchun Long , Dandan Zhao

The difficulty of cross-domain text sentiment classification is that the data distributions in the source domain and the target domain are inconsistent. This paper proposes an attention network based on feature sequences (ANFS) for cross-domain sentiment classification, which focuses on important semantic features by using the attention mechanism. Particularly, ANFS uses a three-layer convolutional neural network (CNN) to perform deep feature extraction on the text, and then uses a bidirectional long short-term memory (BiLSTM) to capture the long-term dependency relationship among the text feature sequences. We first transfer the ANFS model trained on the source domain to the target domain and share the parameters of the convolutional layer; then we use a small amount of labeled target domain data to fine-tune the model of the BiLSTM layer and the attention layer. The experimental results on cross-domain sentiment analysis tasks demonstrate that ANFS can significantly outperform the state-of-the-art methods for cross-domain sentiment classification problems.

中文翻译:

基于特征序列的注意力网络用于跨域情感分类

跨域文本情感分类的困难在于源域和目标域中的数据分布不一致。本文提出了一种基于特征序列的注意力网络,用于跨域情感分类,它利用注意力机制关注重要的语义特征。特别是,ANFS使用三层卷积神经网络(CNN)对文本执行深度特征提取,然后使用双向长短期记忆(BiLSTM)捕获文本特征序列之间的长期依赖关系。我们首先将在源域上训练的ANFS模型转移到目标域,并共享卷积层的参数。然后我们使用少量标记的目标域数据来微调BiLSTM层和关注层的模型。关于跨域情感分析任务的实验结果表明,ANFS可以大大胜过跨域情感分类问题的最新方法。
更新日期:2021-04-23
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