Emotion Wheel Attention-Based Emotion Distribution Learning

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.

Metaverse-Powered Experiential Situational English-Teaching Design: An Emotion-Based Analysis Method

“…In Song et al (2020) , the authors proposed a dynamic graph CNN-based multi-channel EEG emotion recognition method. In Zeng et al (2021) , the authors proposed an emotional wheel attention-based emotion distribution learning model (EWAEDL). In Gao et al (2022) , the authors proposed a novel refined-detrended fluctuation analysis method, that was, multi-order detrended fluctuation analysis (MODFA).…”

“…The experiment was driven on the SEED-IV dataset, using different movie clips as a library of four emotions (peace, sadness, fear, and happiness) for a total of 15 participants. EWAEDL ( Zeng et al, 2021 ), MODFA ( Gao et al, 2022 ), EmoBed ( Han et al, 2021 ), and the proposed method were compared on the SEED-IV dataset.…”

AMIGOS: a robust emotion detection framework through Gaussian ResiNet

“…The inclination model (EM) comprises different enthusiastic states that happen with people. Different passionate models are by and by [3]. The review talks about the layered enthusiastic model and discrete passionate model called DEM and so forth a discrete computing model addresses the emotions put in the different classification.…”