Abstract
Emotion distribution learning is an effective multi-emotion analysis model proposed in recent years. Its core idea is to record the expression degree of examples on each emotion through emotion distribution, which is suitable for handling emotion analysis tasks with emotional ambiguity. To solve the problem that the prior knowledge of emotion psychology is seldom considered in the existing emotion distribution learning methods, we propose an Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model. EWA-EDL generates a prior emotion distribution describing the relevance of emotional psychology for each basic emotion, and then directly integrates the prior knowledge based on the emotion wheel into the deep neural network through the attention mechanism. The deep network of EWA-EDL is trained using an end-to-end approach to learn both emotion distribution prediction and emotion classification tasks. The EWA-EDL architecture includes five main parts: input layer, convolutional layer, pooling layer, attention layer and multi-task loss layer. Extensive comparative experiments on eight commonly used textual emotion datasets show that EWA-EDL outperforms the comparison emotion distribution learning methods on both emotion distribution prediction and emotion classification task.
Highlights
The task goal of emotion analysis is to uncover the emotional tendencies of people embedded in data [1], [2], which has important applications in many emerging artificial intelligence scenarios such as personalized recommendation [3] and intelligent customer service systems [4]
To effectively introduce psychological prior knowledge into the EDL model to improve the performance of emotion analysis, we propose the Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model in this paper
Experiments To examine the performance of the EWA-EDL model proposed in this paper, three sets of experiments were conducted to analyze the effect of the parameter σ of the prior emotion distribution on the performance of the EWA-EDL model, to compare the emotion prediction performance of multiple EDL methods on the English and Chinese datasets and to compare the emotion classification performance of three deep network-based EDL models on seven single-label datasets
Summary
The task goal of emotion analysis is to uncover the emotional tendencies of people embedded in data [1], [2], which has important applications in many emerging artificial intelligence scenarios such as personalized recommendation [3] and intelligent customer service systems [4]. The interval angle on the emotion wheel represents the degree of psychological correlation between the corresponding emotions He and Jin proposed a graph convolutional neural network-based EDL approach considering psychological prior knowledge on image emotion recognition task in 2019 with good results [12]. To effectively introduce psychological prior knowledge into the EDL model to improve the performance of emotion analysis, we propose the Emotion Wheel Attention based Emotion Distribution Learning (EWA-EDL) model in this paper. (1) The attention mechanism is used to directly integrate the prior knowledge of psychology based on emotion wheels into the deep learning network, and proposed the EWA-EDL model. There is no EDL work that uses the attention mechanism to directly integrate the prior knowledge expect joy anger trust disgust fear sadness surprise
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
