4.6 Article

Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

期刊

IEEE ACCESS
卷 9, 期 -, 页码 15561-15569

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3052937

关键词

Aspect-based sentiment analysis; online reviews; neural networks

资金

  1. National Natural Science Foundation of China [61471083]
  2. Dalian Science and Technology Innovation Foundation Project [2018J11CY009]

向作者/读者索取更多资源

ABSA is a technique for identifying views and sentiment polarities towards a given aspect in reviews, which has become an important task in natural language understanding. The sentiment polarity of a sentence is significantly correlated with the targeted aspect.
Aspect-based sentiment analysis (ABSA) aims to identify views and sentiment polarities towards a given aspect in reviews. Compared with general sentiment analysis, ABSA can provide more detailed and complete information. Recently, ABSA has become an important task for natural language understanding and has attracted considerable attention from both academic and industry fields. The sentiment polarity of a sentence is not only decided by its content but also has a relatively significant correlation with the targeted aspect. For this reason, we propose a model for aspect-based sentiment analysis which is a combination of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), utilizing the local features generated by CNN and the long-term dependency learned by GRU. Extensive experiments have been conducted on datasets of hotels and cars, and results show that the proposed model achieves excellent performance in terms of aspect extraction and sentiment classification. Experiments also demonstrate the great domain expansion capability of the model.

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