Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (8): 98-106    DOI: 10.11925/infotech.2096-3467.2019.1243
Current Issue | Archive | Adv Search |
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
Download: PDF (1011 KB)   HTML ( 9
Export: BibTeX | EndNote (RIS)      

[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.

URL:     OR

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
[1] Rout J K, Choo K K R, Dash A K, et al. A Model for Sentiment and Emotion Analysis of Unstructured Social Media Text[J]. Electronic Commerce Research, 2018,18:181-199.
doi: 10.1007/s10660-017-9257-8
[2] Peng M L, Zhang Q, Jiang Y G, et al. Cross-Domain Sentiment Classification with Target Domain Specific Information[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics.Melbourne, Australia, 2018: 2505-2513.
[3] 刘全, 梁斌, 徐进, 等. 一种基于方面情感分析的深度分层网络模型[J]. 计算机学报, 2018,41(12):2637-2652.
[3] ( Liu Quan, Liang Bin, Xu Jin, et al. A Deep Layered Network Model for Aspect-Based Sentiment Analysis[J]. Chinese Journal of Computers, 2018,41(12):2637-2652.)
[4] 张庆庆, 贺兴时, 王慧敏, 等. 基于深度信念网络的文本情感分类研究[J]. 数据分析与知识发现, 2019,3(4):71-79.
[4] ( Zhang Qingqing, He Xingshi, Wang Huimin, et al. Text Sentiment Classification Based on Deep Belief Network[J]. Data Analysis and Knowledge Discovery, 2019,3(4):71-79.)
[5] Chen Y, Hou W J, Cheng X Y, et al. Joint Learning for Emotion Classification and Emotion Cause Detection[C]// Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018: 646-651.
[6] Li X J, Feng S, Wang D L, et al. Context-Aware Emotion Cause Analysis with Multi-Attention-Based Neural Network[J]. Knowledge-Based Systems, 2019,174:205-218.
doi: 10.1016/j.knosys.2019.03.008
[7] Lee S Y M, Chen Y, Huang C R, et al. Detecting Emotion Causes with a Linguistic Rule-Based Approach[J]. Computational Intelligence, 2013,29(3):390-416.
doi: 10.1111/j.1467-8640.2012.00459.x
[8] Yada S, Ikeda K, Hoashi K, et al. A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction[C]// Proceedings of 2017 IEEE International Conference on Data Mining Workshops. IEEE, 2017: 414-421.
[9] Gao K, Xu H, Wang J S. A Rule-Based Approach to Emotion Cause Detection for Chinese Micro-Blogs[J]. Expert Systems with Applications, 2015,42(9):4517-4528.
doi: 10.1016/j.eswa.2015.01.064
[10] Chen Y, Hou W J, Cheng X Y. Hierarchical Convolution Neural Network for Emotion Cause Detection on Microblogs[C]// Proceedings of the 27th International Conference on Artificial Neural Networks. 2018: 115-122.
[11] Gui L, Hu J N, He Y L, et al. A Question Answering Approach for Emotion Cause Extraction[C]// Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 1593-1602.
[12] Weston J, Chopra S, Bordes A. Memory Networks[OL]. arXiv Preprint, arXiv: 1410.3916.
[13] Yu X Y, Rong W G, Zhang Z, et al. Multiple Level Hierarchical Network-Based Clause Selection for Emotion Cause Extraction[J]. IEEE Access, 2019,7:9071-9079.
doi: 10.1109/ACCESS.2018.2890390
[14] Xia R, Ding Z X. Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts[C]// Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019: 1003-1012.
[15] 苏剑林. 基于CNN的阅读理解式问答模型[EB/OL]. [2018-04-15].
[15] ( Su Jianlin. CNN Based Reading Comprehension Question and Answer Model[EB/OL]. [2018-04-15].
[16] Kim Y. Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1746-1751.
[17] Laskar Z, Kannala J. Context Aware Query Image Representation for Particular Object Retrieval[C]// Proceedings of Scandinavian Conference on Image Analysis. 2017: 88-99.
[18] Dauphin Y N, Fan A, Auli M, et al. Language Modeling with Gated Convolutional Networks[C]// Proceedings of the 34th International Conference on Machine Learning. 2017: 933-941.
[19] Wang W H, Yang N, Wei F R, et al. Gated Self-Matching Networks for Reading Comprehension and Question Answering[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 189-198.
[20] Lin T Y, Goyal P, Girshick R, et al. Focal Loss for Dense Object Detection[C]// Proceedings of the IEEE International Conference on Computer Vision. 2017: 2980-2988.
[21] Li S, Zhao Z, Hu R F, et al. Analogical Reasoning on Chinese Morphological and Semantic Relations[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018: 138-143.
[22] Hochreiter S, Schmidhuber J. Long Short-Term Memory[J]. Neural Computation, 1997,9(8):1735-1780.
doi: 10.1162/neco.1997.9.8.1735 pmid: 9377276
[1] Yu Bengong, Zhu Mengdi. Question Classification Based on Bidirectional GRU with Hierarchical Attention and Multi-channel Convolution[J]. 数据分析与知识发现, 2020, 4(8): 50-62.
[2] Yu Chuanming, Wang Manyi, Lin Hongjun, Zhu Xingyu, Huang Tingting, An Lu. A Comparative Study of Word Representation Models Based on Deep Learning[J]. 数据分析与知识发现, 2020, 4(8): 28-40.
[3] Wang Sili, Zhu Zhongming, Yang Heng, Liu Wei. Research on Automatic Identification of Hypernym-Hyponym Relations of Domain Concepts Based on Pattern and Projection Learning [J]. 数据分析与知识发现, 0, (): 1-.
[4] Weng Mengjuan,Yao Changqing,Han Hongqi,Wang Lijun,Ran Yaxin. Classification and Indexing Method with CNN for Imbalanced Datasets[J]. 数据分析与知识发现, 2020, 4(7): 87-95.
[5] Tang Xiaobo,Gao Hexuan. Classification of Health Questions Based on Vector Extension of Keywords[J]. 数据分析与知识发现, 2020, 4(7): 66-75.
[6] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[7] Wang Jiandong,Yu Shiyang. Principles on Constructing National Economic Brain[J]. 数据分析与知识发现, 2020, 4(7): 2-17.
[8] Xu Hongxia,Yu Qianqian,Qian Li. Studying Content Interaction Data with Topic Model and Sentiment Analysis[J]. 数据分析与知识发现, 2020, 4(7): 110-117.
[9] Li Guangjian, Wang Kai, Zhang Qingzhi. The Analysis Framework Based on the Multi-source Data for US Export Control with the Empirical Study [J]. 数据分析与知识发现, 0, (): 1-.
[10] Shao Qi, Mu Dongme, Wang Ping, Jin Chunyan. Semantic-based Subject Discovery of Public Health Emergencies Network Public Opinion [J]. 数据分析与知识发现, 0, (): 1-.
[11] Li Keyu,Wang Hao,Gong Lijuan,Tang Huihui. Measurement and Distribution of Index Quality in Research Topics from Academic Databases[J]. 数据分析与知识发现, 2020, 4(6): 91-108.
[12] Wei Tingxin,Bai Wenlei,Qu Weiguang. Sense Prediction for Chinese OOV Based on Word Embedding and Semantic Knowledge[J]. 数据分析与知识发现, 2020, 4(6): 109-117.
[13] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[14] Jiao Qihang,Le Xiaoqiu. Generating Sentences of Contrast Relationship[J]. 数据分析与知识发现, 2020, 4(6): 43-50.
[15] Cai Yongming,Liu Lu,Wang Kewei. Identifying Key Users and Topics from Online Learning Community[J]. 数据分析与知识发现, 2020, 4(6): 69-79.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938