|
|
|
| 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 |
|
|
|
|
Abstract [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.
|
|
Received: 14 November 2019
Published: 05 June 2020
|
|
|
|
Corresponding Authors:
Dai Jianhua
E-mail: baoping@njau.edu.cn
|
| [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].https://spaces.ac.cn/archives/5409.
|
| [15] |
( Su Jianlin. CNN Based Reading Comprehension Question and Answer Model[EB/OL]. [2018-04-15].https://spaces.ac.cn/archives/5409.
|
| [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
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
