Multi-aspect Sentiment Attention Modeling for Sentiment Classification of Educational Big Data

被引：0
|
作者：
Zhai G. [1 ,2 ]
Yang Y. [1 ,2 ]
Wang H. [1 ,2 ]
Du S. [1 ,2 ]
机构：
[1] School of Information Science and Technology, Southwest Jiao- tong University, Chengdu
[2] Key Laboratory of Cloud Computing and Intelligent Techni-que, Sichuan Province, Southwest Jiaotong University, Chengdu
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Attention Mechanism; Educational Big Data; Neural Network; Sentiment Analysis;
D O I：
10.16451/j.cnki.issn1003-6059.201909007
中图分类号：
学科分类号：
摘要：
Aiming at inefficiency and heavy workloads of college curriculum evaluation methods, a multi-aspect sentiment attention modeling(multi-ASAM) is proposed. Multi-ASAM concatenates a sentence and various aspects of the sentence by neural networks and adds emotional resources attention. To achieve better classification results, influence of relationships between aspects on emotinal polarity and contribution of emotional resources to emotional polarity is taken into auount in multi-ASAM. Experimental results show that Multi-ASAM is improved compared with other methods in the application of education and other fields. © 2019, Science Press. All right reserved.
引用
收藏
页码：828 / 834
页数：6
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