Integrating P lutchik’s Theory with Mixture of Experts for Enhancing Emotion Classification (2024)

Abstract: Emotion significantly influences human behavior and decision-making processes. We propose a labeling methodology grounded in Plutchik’s Wheel of Emotions theory for emotion classification. Furthermore, we employ a Mixture of Experts (MoE) architecture to evaluate the efficacy of this labeling approach, by identifying the specific emotions that each expert learns to classify. Experimental results reveal that our methodology improves the performance of emotion classification.

## introduction emotion is essential in human life, having influence on our thoughts, behaviors, and communication. recognizing the paramount importance of emotions, researchers have made significant efforts to analyze and understand them @xcite . a particularly important area of this research is emotion recognition in text, as it forms a substantial part of our daily interactions, including email and social network service (sns). while sentiment analysis, categorizing text as positive, negative, or neutral, has advanced significantly, recognizing the full spectrum of emotions in text-such as joy, anger, sadness, and fear-remains a challenging task. @xcite report that roberta large with hg-f24 achieved 84.7% accuracy on sentiment analysis of amazon product reviews but only 40.9% accuracy in emotion detection using a twitter (x) dataset. previous research utilizing deep learning technology has demonstrated significant promise in extracting emotions from text @xcite @xcite @xcite . recently, @xcite conducted a study analyzing the role of emotions in controversial reddit comments using language models. @xcite systematically * corresponding author. measured the affective alignment of language models (lms) by comparing lm-generated responses to snss on two socio-political issues. however, these studies face challenges like sampling bias and subjective annotation. for instance, @xcite note that existing multi-label text classification models lack the ability to generalize complex concepts. @xcite argue that current methods overlook the sentiment polarity of words. to tackle the problems in emotion annotation, we introduce a new labeling approach. our primary objective is to enhance the expressiveness of emotion labels by applying plutchik's wheel of emotions and diagram of emotion dyads. furthermore, we employ a mixture of experts (moe) framework for emotion classification, which identifies the specific emotion that each expert in the model is best at classifying. this approach seeks to validate the improved classification performance and specialization of experts in distinct emotional categories. the key contributions of this research are listed as follows: the structure of the paper is organized as follows. section 2 provides a review of related work. section 3 outlines our approach. section 4 details @xcite . @xcite . the experimental design. section 5 discusses the results, and section 6 provides an in-depth analysis. the final section concludes with future research. ## method this section describes our proposed method for emotion classification, utilizing the new labeling method based on plutchik's emotion theory and the implementation of the moe structure in our model. ## experiments this section details the experimental design for evaluating the effectiveness of the proposed method in multi-label emotion classification. ## analysis we investigated the relationships between emotions by analyzing the predominant expert selections for each. by tracking the output values of the gate layer in a mixture of experts (moe) model, we identified which experts were primarily selected for each emotion. our approach involved selecting experts for each token and aggregating the selection proportions of the top-k experts per token for each input. the value of k corresponds to the top-k used in the moe, with the selection proportions for each token summing to 1. inputs were grouped by their emotions labels, and the aggregate expert selection proportions for each label were computed and standardized. using these frequencies of expert selections for each emotion, we plotted emotionemotion correlations to examine the relationships between emotions. figure 5a shows that joy, love, and optimism exhibit strong correlations, indicating that positive emotions are closely interconnected in the semeval-2018 dataset. in contrast, anger, sadness, and disgust show strong positive correlations with each other, as well as with fear and pessimism, forming a cluster of negative emotions. additionally, optimism and pessimism, as well as love and sadness, show strong negative correlations with each other, indicating that these emotions have opposite characteristics. furthermore, love tends to have high correlations with joy and trust, optimism with joy, and pessimism with anticipation and sadness. these patterns also allow us to understand the similarities between complex emotions and their component basic emotions. in goemotions, as shown in figure 5b , joy, love, optimism, and admiration exhibit strong positive correlations, indicating their close interrelation as positive emotions. conversely, anger, annoyance, excitement, fear, grief, and pride form a group of negative emotions, with admiration and anger showing a strong negative correlation, highlighting their opposing nature. additionally, the complex emotions disappointment and curiosity correlate highly with sadness and surprise, respectively, while anger correlates strongly with annoyance and sadness with grief. these patterns reveal the similarities between complex emotions and their component emotions, as well as the relationships between basic emotions and their milder or more intense counterparts. overall, while the selection of experts for each emotion does not perfectly align with plutchik's emotion theory, the results show a significant degree of similarity. this suggests that our approach is effective for emotion analysis. these findings contribute to a deeper understanding of emotional interrelations and can aid in improving emotion prediction models. ## conclusion our approach, grounded in plutchik's emotion theory and utilizing the moe architecture, significantly enhances the performance of multi-label emotion classification tasks. the proposed methodologies were evaluated against baseline models, demonstrating significant improvements in classification accuracy. notably, our approach excelled in identifying emotions that are traditionally difficult to classify and showed superior performance in recognizing complex emotions. moreover, the analysis of expert selection tendencies, based on emotion correlations, revealed that our model's behavior closely aligns with plutchik's emotion theory. this alignment not only enhances classification accuracy but also provides a theoretically grounded insight into emotional interactions. thus, we believe that our research presents a robust framework for multi-label emotion classification, integrating psychological theories and advanced machine learning techniques in emotion recognition tasks. future research could focus on refining the classification of mild and intense variations of emotions.