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| 基于词嵌入与记忆网络的方面情感分类 | |
| 引用本文： | 刘一伊,张瑾,余智华,刘悦,程学旗.基于词嵌入与记忆网络的方面情感分类[J].模式识别与人工智能,2019,32(12):1093-1099. |
| 作者姓名： | 刘一伊 张瑾 余智华 刘悦 程学旗 |
| 作者单位： | 1.中国科学院大学 计算机科学与技术学院 北京 100049; 2.中国科学院计算技术研究所 中国科学院网络数据科学与技术重点实验室 北京 100190; 3.中国科学院计算技术研究所烟台分所 烟台中科网络技术研究所 烟台 264005 |
| 基金项目： | 西藏自治区科技计划项目(No.XZ201801-GB-17)、泰山学者工程专项经费(No.ts201511082)资助 |
| 摘 要： | 用户评论往往同时包含多个方面、多种情感,如何正确判断一条评论中不同方面的情感倾向性是方面情感分析的难点.文中提出基于词嵌入与记忆网络的方面情感分类.通过在记忆网络的不同模块引入方面词向量,加强方面词的语义信息,指导注意力机制捕捉方面相关的上下文信息,提升方面情感分类效果.在SemEval 2014任务4的短文本英文评论数据集和文中标注的长文本中文新闻数据集上实验表明,文中方法分类效果较好,在记忆网络框架下引入方面词嵌入信息是有效的. |
| 关 键 词： | 方面情感分类 词嵌入 记忆神经网络 注意力机制 |
| 收稿时间： | 2019-10-18 |
Aspect Embedding on Memory Network for Aspect Sentiment Classification |
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| LIU Yiyi,ZHANG Jin,YU Zhihua,LIU Yue,CHENG Xueqi.Aspect Embedding on Memory Network for Aspect Sentiment Classification[J].Pattern Recognition and Artificial Intelligence,2019,32(12):1093-1099. | |
| Authors: | LIU Yiyi ZHANG Jin YU Zhihua LIU Yue CHENG Xueqi |
| Affiliation: | 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. |
| Keywords: | Aspect Sentiment Classification Aspect Embedding Memory Neural Network Attention Mechanism |
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