Dependency-Aware Attention Model for Emotion Analysis for Online News

  • Xue Zhao
  • Ying ZhangEmail author
  • Xiaojie Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11439)


This paper studies the emotion responses evoked by the news articles. Most work focuses on extracting effective features from text for emotion classification. As a result, the valuable information contained in the emotion labels has been largely neglected. In addition, all words are potentially conveying affective meaning yet they are not equally significant. Traditional attention mechanism can be leveraged to extract important words according to the word-label co-occurrence pattern. However, words that are important to the less popular emotions are still difficult to identify. Because emotions have intrinsic correlations, by integrating such correlations into attention mechanism, emotion triggering words can be detected more accurately. In this paper, we come up with an emotion dependency-aware attention model, which makes the best use of label information and the emotion dependency prior knowledge. The experiments on two public news datasets have proved the effectiveness of the proposed model.


Emotion analysis Attention mechanism Neural sentiment analysis 



We thank the reviewers for their constructive comments. This research is supported by National Natural Science Foundation of China (No. U1836109), Natural Science Foundation of Tianjin (No. 16JCQNJC00500) and Fundamental Research Funds for the Central Universities.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer ScienceNankai UniversityTianjinPeople’s Republic of China

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