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

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.