Affiliations: [a] College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| [b] College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
Correspondence:
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Corresponding authors. Zhongying Zhao, College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China. Tel.: +86 532 86057524; Fax: +86 532 86057758; E-mail: [email protected] and Chao Li, College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China. E-mail: [email protected].
Abstract: Aspect-based sentiment classification, a fine-grained sentiment analysis task, aims to predict the sentiment polarity for a specified aspect. However, the existing aspect-based sentiment classification approaches cannot fully model the dependency-relationship between words and are easily disturbed by irrelevant aspects. To address this problem, we propose a novel approach named Dependency-Relationship Embedding and Attention Mechanism-based LSTM. DA-LSTM first merges the word hidden vector output by LSTM with the dependency-relationship embedding to form a combined vector. This vector is then fed into the attention mechanism together with the aspect information which can avoid interference to calculate the final word representation for sentiment classification. Our extensive experiments on benchmark data sets clearly show the effectiveness of DA-LSTM.
