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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (8): 98-106    DOI: 10.11925/infotech.2096-3467.2019.1243
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Extracting Emotion-Cause Pairs Based on Emotional Dilation Gated CNN
Dai Jianhua1,2,3(),Deng Yubin1,3
1Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China
2Research Institute of Languages and Cultures, Hunan Normal University, Changsha 410081, China
3College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
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[Objective] This paper proposes an Emotional Dilation Gated CNN (EDGCNN) model, aiming to extract emotion-cause pairs for sentiment analysis. [Methods] First, we used the emotional discriminant model to identify sentiment sentences. Then, we input coding for these sentences to the EDGCNN model and located corresponding reasons. Finally, we tagged keywords of reasons generated from the experimental dataset. [Results] The new model’s recall and F1 values reached 63.52% and 60.45% respectively on the training dataset, which were better or very similiar to the existing ones The proposed model also extracted emotion-cause pairs at finergranularity level. [Limitations] The experimental corpus size was small. [Conclusions] The proposed model can extract emotion-cause pairs effectively.

Key wordsEmotion-Cause Pair Extraction      EDGCNN      Emotion Discrimination     
Received: 14 November 2019      Published: 05 June 2020
ZTFLH:  TP391  
Corresponding Authors: Dai Jianhua     E-mail:

Cite this article:

Dai Jianhua, Deng Yubin. Extracting Emotion-Cause Pairs Based on Emotional Dilation Gated CNN. Data Analysis and Knowledge Discovery, 2020, 4(8): 98-106.

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Example of ECPE Task
Experimental Considerations
Emotional Discriminant Model
Emotion-Cause Pair Extraction Model
Dilated Convolutions
数据集 数量 比例
只有一对情感-原因对的文本 1 746 89.77%
有两对情感-原因对的文本 177 9.10%
超过两对情感-原因对的文本 22 1.13%
总计 1 945 100%
The Proportion of Documents with Different Number of Emotion-Cause Pairs
Example of Emotion Cause Keyword Tagging
实验模型 情感句判别结果
P R F1
CNN 0.734 0 0.876 9 0.799 1
LSTM 0.674 4 0.824 6 0.741 9
Result of Emotional Sentences Discrimination
实验模型 情感原因对提取结果
P R F1
Indep 0.683 2 0.508 2 0.581 8
Inter-CE 0.690 2 0.513 5 0.590 1
Inter-EC 0.672 1 0.570 5 0.612 8
EDGCNN 0.575 8 0.635 2 0.604 5
Experimental Results
文本中部分语句 情感句 EDGCNN
9 跃陷入回忆 9-8
3,happiness,她为自己再过几天就可基本康复出院而感到高兴 3 康复出院 3-3
4 民警守在身边 4-6
2 血库告急的消息 1-2
7 提出离婚 7-6
20 数落女婿 20-19
Experimental Results of EDGCNN Model
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