Affiliations: [a] School of Computer and Communication Engineering, Changsha University of Science and Technology, P. R. China | [b] Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, P.R. China | [c] School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Tamil Nadu, India
Correspondence:
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Corresponding author. Jin Wang, School of Computer and Communication Engineering, Changsha University of Science and Technology, P.R. China. Tel./Fax: +86 0731 85258462; E-mail:jinwang@csust.edu.cn.
Abstract: Recently, sentiment analysis has become a focus domain in artificial intelligence owing to the massive text reviews of modern networks. The fast increase of the domain has led to the spring up of assorted sub-areas, researchers are also focusing on subareas at various levels. This paper focuses on the key subtask in sentiment analysis: aspect-based sentiment analysis. Unlike feature-based traditional approaches and long short-term memory network based models, our work combines the strengths of linguistic resources and gating mechanism to propose an effective convolutional neural network based model for aspect-based sentiment analysis. First, the proposed regularizers from the real world linguistic resources can be of benefit to identify the aspect sentiment polarity. Second, under the guidance of the given aspect, the gating mechanism can better control the sentiment features. Last, the basic structure of model is convolutional neural network, which can perform parallel operations well in the training process. Experimental results on SemEval 2014 Restaurant Datasets demonstrate our approach can achieve excellent results on aspect-based sentiment analysis.
