Aspect Embedding on Memory Network for Aspect Sentiment Classification
LIU Yiyi1,2, ZHANG Jin2, YU Zhihua2, LIU Yue2, CHENG Xueqi3
1.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049; 2.Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190; 3.Institute of Network Technology, Institute of Computing Technology(YANTAI), Chinese Academy of Sciences, Yantai264005
Abstract A comment contains multiple aspects and sentiments, and therefore it is difficult to classify sentiment polarity of different aspects correctly. A model combining aspect embedding with memory neural network is proposed to identify the sentiment of aspects in a comment. The aspect word vector is introduced into different modules of the memory network. The semantic information of the word is reinforced. The attention mechanism is guided to capture the relevant context, and thus the sentiment classification effect in the aspect is improved. Experimental results on short text English comments of SemEval 2014 Task 4 dataset and the long-text Chinese news dataset indicate that the proposed method achieves good classification effect and fully verifies the validity of the word embedding information introduced into the framework of the memory network.
Fund:Supported by Science and Technology Plan Project of Xizang(Tibet) Autonomous Region(No.XZ201801-GB-17), Taishan Scholars Program of Shandong Province(No.ts201511082)
Corresponding Authors:
ZHANG Jin, Ph.D., associate professor. His research interests include public opinion analysis and natural language processing.
About author:: LIU Yiyi, master student. Her research interests include natural language processing and sentiment analysis.YU Zhihua, Ph.D., professor. His research interests include network information processing and information security.LIU Yue, Ph.D., associate professor. Her research interests include information retrieval and big data.CHENG Xueqi, Ph.D., professor. His research interests include big data analysis and mining.
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2019, Vol. 32 
