中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Bidirectional-GRU based on attention mechanism for aspect-level Sentiment Analysis

文献类型:会议论文

作者Zhang DY(张丁一)2,3; Zhai PH(翟鹏华)1,2,3
出版日期2019
会议日期February 22-24, 2019
会议地点Zhuhai, China
关键词sentimental analysis deep learning machine learning attention mechanism aspect-level
页码86-90
英文摘要Aspect-level sentiment analysis is a fine-grained natural language processing task. For traditional deep learning models, they cannot accurately construct the aspect-level sentiment features. Such as, for the sentence of “the movie is very funny, but the seats in the theater is uncomfortable.” For the movie, the polarity is positive, but it is negative for seats. To deal with this problem, we propose a bidirectional gated recurrent units neural network model that integrates the attention mechanism to solve the task of aspect-level sentiment analysis. The attention mechanism can focus on the different parts of a sentence when the sentence has several different aspects. Because we use a bidirectional gated recurrent unit, we can get independent context semantic information and get the deeper aspect sentiment information from the front and back, so that we can deal with the specific aspect sentiment polarity. Finally, we experiment on SemEval-2014 dataset and twitter dataset, the result of experiments verified the effectiveness of attention-based bidirectional gated recurrent unit on the aspect sentiment analysis. The model achieves good performance at different datasets and has further improvement comparing to previous models.
源文献作者Asia Society of Researchers ; Metropolitan State University of Denver ; Southwest Jiaotong University ; University of Macau
产权排序1
会议录ACM International Conference Proceeding Series
会议录出版者ACM
会议录出版地New York
语种英语
ISBN号978-1-4503-6600-7
源URL[http://ir.sia.cn/handle/173321/24685]  
专题沈阳自动化研究所_数字工厂研究室
通讯作者Zhai PH(翟鹏华)
作者单位1.University of Chinese Academy of Science, Beijing 100049, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
推荐引用方式
GB/T 7714
Zhang DY,Zhai PH. Bidirectional-GRU based on attention mechanism for aspect-level Sentiment Analysis[C]. 见:. Zhuhai, China. February 22-24, 2019.

入库方式: OAI收割

来源:沈阳自动化研究所

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