An attention network based on feature sequences for cross-domain sentiment classification

Article type: Research Article

Authors: Meng, Jiana | Dong, Yu^* | Long, Yingchun | Zhao, Dandan

Affiliations: School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning, China

Correspondence: [*] Corresponding author: Yu Dong, Dalian Minzu University, Liaohexi Road, Dalian, Liaoning 116600, China. Tel.: +86 13342283609; E-mail: [email protected].

Abstract: 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.

Keywords: Deep transfer learning, CNN, BiLSTM, attention mechanism, sentiment classification

DOI: 10.3233/IDA-205130

Journal: Intelligent Data Analysis, vol. 25, no. 3, pp. 627-640, 2021