Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (3): 159-164.DOI: 10.3778/j.issn.1002-8331.1811-0120

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Aspect-Based Memory Network for Fine-Grained Product Sentiment Analysis

LI Jinyuan, KANG Yan, YANG Qiyue, WANG Peiyao, CUI Guorong   

  1. Department of Software, Yunnan University, Kunming 650500, China
  • Online:2020-02-01 Published:2020-01-20



  1. 云南大学 软件学院,昆明 650500

Abstract: The design and marketing positioning of products based on the emotional needs of users has become a research hotspot, and fine-grained emotional mining can further improve the efficiency of comment analysis. This paper proposes an aspect-oriented deep memory network model for fine-grained sentiment analysis. The IT product review data of is crawled, the dependency syntax analysis method is used to extract the aspect words in the comments, the aspect-based fine-grained emotional classification is achieved by using the deep memory network model based on the self-attention mechanism effectively. The experimental results show that the accuracy of the aspect-oriented deep memory network model on the English dataset is improved compared with some classical models. At the same time, in the Jingdong and other 40?000 IT user evaluation data for emotional sentiment analysis also has a good effect.

Key words: deep memory network, self-attention mechanism, fine-grained sentiment analysis, dependency syntax analysis method, emotional needs

摘要: 以用户情感需求为导向进行产品的设计和营销定位已成为研究热点,细粒度的情感挖掘可进一步提高评论分析的效率。提出一种面向方面深度记忆网络模型进行细粒度情感分析。对京东等IT产品评论数据进行爬取,应用依存句法分析方法抽取评论中的方面词,采用基于self-attention机制的深度记忆网络模型实现基于方面的细粒度情感分类。实验结果表明,面向方面深度记忆网络模型在英文数据集上的准确率相比一些经典模型有所提升,同时在京东等40?000条IT的用户评价数据进行情感倾向分析也具有良好的效果。

关键词: 深度记忆网络, self-attention机制, 细粒度情感分析, 依存句法分析, 情感需求