SCA-ECPE: Emotion-Cause Pair Extraction Based on Sentiment Clustering Analysis

Xu, Xin; Wu, Houyue; Zhu, Guangli

doi:10.1007/978-3-031-28893-7_27

Lecture Notes on Data Engineering and Communications Technologies

2023

DOI: 10.1007/978-3-031-28893-7_27

|View full text |Cite

SCA-ECPE: Emotion-Cause Pair Extraction Based on Sentiment Clustering Analysis

Xin Xu

Houyue Wu

Guangli Zhu

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

Zhang

et al. 2023

COMSIS J

View full text Add to dashboard Cite

As an emotional cause detection task, Emotion-Cause Pair Extraction (ECPE) provides technical support for intelligent psychological counseling, empty-nest elderly care, and other fields. Current approaches mainly focus on extracting by recognizing causal relationships between clauses. Different from these existing methods, this paper further considers the influence of sentimental intensity to improve extraction accuracy. To address this issue, we propose an extraction model based on sentiment analysis and 3D Convolutional Neural Networks (3D-CNN), named SEE-3D. First, to prepare fundamental data for sentiment analysis, emotion clauses are clustered into six emotion domains according to six emotion types in the ECPE dataset. Then, a pre-trained sentiment analysis model is introduced to compute emotional similarity, which provides a reference for identifying emotion clauses. In the extraction process, similar features of adjacent documents in the same batch of samples are fused as input of 3D-CNN. The 3D-CNN enhances the macro semantic understanding ability of the model, thereby improving the extraction performance. The results of experiments show that the accuracy of ECPE can be effectively improved by the SEE-3D model.

show abstract

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

Zhang

et al. 2023

COMSIS J

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

SCA-ECPE: Emotion-Cause Pair Extraction Based on Sentiment Clustering Analysis

Cited by 1 publication

References 9 publications

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

SEE-3D: Sentiment-driven emotion-cause pair extraction based on 3D-CNN

Contact Info

Product

Resources

About