Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 104-110.DOI: 10.3778/j.issn.1002-8331.1906-0418

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Interactive Attention Networks for Target-Based Sentiment Analysis

HAN Hu, LIU Guoli   

  1. 1.School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
    2.Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou 730070, China
  • Online:2020-09-15 Published:2020-09-10



  1. 1.兰州交通大学 电子与信息工程学院,兰州 730070
    2.甘肃省人工智能与图形图像工程研究中心,兰州 730070


Target-based sentiment analysis aims to identify the sentiment polarity of specific target in its context. In recent years, more and more researchers use various methods based on neural networks to solve target-based sentiment analysis, and achieve success. However, most target-related models only focus on the impact of target on context modeling, but ignore the role of context in target modeling. To address this problem, this paper proposes an interactive attention networks named LT-T-TR for target-based sentiment analysis, which divides a review into three parts:left context with target phrase, target phrase, and right context with target phrase. The interaction between target phrase and left/right context is utilized by attention mechanism to learn the representations of target phrase and left/right context separately. As a result, the most important words in target phrase or in left/right context are captured, and the results on laptop and restaurant datasets demonstrate the effectiveness of the model.

Key words: target-based sentiment analysis, interactive attention networks, attention mechanism



关键词: 特定目标情感分析, 交互注意力网络, 注意力机制