Sentiment Classification with Gated CNN for Customer Reviews

被引:0
作者
Okada, Makoto [1 ]
Yanagimoto, Hidekazu [2 ]
Hashimoto, Kiyota [3 ]
机构
[1] Osaka Prefecture Univ, Grad Sch Engn, Naka Ku, 1-1 Gakuen Cho, Sakai, Osaka 5998531, Japan
[2] Osaka Prefecture Univ, Coll Sustainable Syst Sci, Naka Ku, 1-1 Gakuen Cho, Sakai, Osaka 5998531, Japan
[3] Prince Songkla Univ, ESSAND, 80 Moo 1 Vichitsongkram Rd, Kathu 83120, Phuket, Thailand
来源
2018 INTERNATIONAL JOINT SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING (ISAI-NLP 2018) | 2018年
关键词
Sentiment analysis; Gated Convolutional Neural Network; Costumer review;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recurrent neural networks(RNNs) have been applied to sentiment classification but RNNs is usually heavier than convolutional neural networks (CNNs), turning more interest in the application of CNNs to language tasks. In this paper we propose a method to apply gated CNN (gCNN) with Maxpooling to sentiment classification of customer reviews. In our proposal, the application of gCNN is to sentiment classification, instead of constructing a language model. Our experiment is conducted with Amazon Product Review dataset and Japanese review dataset of TripAdvisor. The whole of each review is used as an input, instead of each sentence. The result is that a simple application of gCNN to sentiment classification achieved sufficient accuracies with the two datasets. Thus, an implication is that gCNN is proven to work fine for sentiment classification much faster than RNNs with fine results in the different language datasets.
引用
收藏
页码:163 / 167
页数:5
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