Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 36, Issue 5, Pages 3971-3980Publisher
IOS PRESS
DOI: 10.3233/JIFS-169958
Keywords
Aspect-based sentiment analysis; linguistic resources; convolutional neural networks; gating mechanism
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Funding
- National Natural Science Foundation of China [616020 59, 61772454, 61811530332]
- Hunan Provincial Natural Science Foundation of China [2017JJ3 334]
- Research Foundation of Education Bureau of Hunan Province, China [16C0045]
- Open Project Program of the National Laboratory of Pattern Recognition (NLPR)
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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.
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