EC o K : Emotional Commonsense Knowledge Graph for Mining Emotional Gold (2024)

Abstract: The demand for understanding and expressing emotions in the field of natural language processing is growing rapidly. Knowledge graphs, as an important form of knowledge representation, have been widely utilized in various emotion-related tasks. However, existing knowledge graphs mainly focus on the representation and reasoning of general factual knowledge, while there are still significant deficiencies in the understanding and reasoning of emotional knowledge. In this work, we construct a comprehensive and accurate emotional commonsense knowledge graph, ECoK. We integrate cutting-edge theories from multiple disciplines such as psychology, cognitive science, and linguistics, and combine techniques such as large language models and natural language processing. By mining a large amount of text, dialogue, and sentiment analysis data, we construct rich emotional knowledge and establish the knowledge generation model COMET-ECoK. Experimental results show that ECoK contains high-quality emotional reasoning knowledge, and the performance of our knowledge generation model surpasses GPT-4-Turbo, which can help downstream tasks better understand and reason about emotions. Our data and code is available from https://github.com/ZornWang/ECoK.

## introduction in the field of artificial intelligence, despite the remarkable progress we have made, a core challenge remains: how to endow machines with commonsense reasoning abilities akin to those of humans @xcite . when humans observe an event, they can effortlessly leverage their background knowledge, experience, and intuition to predict and reason about the unobserved causes and effects related to that event. this capability comes naturally to address this limitation, commonsense knowledge graphs have been proposed as a solution. commonsense knowledge graph is a graph structure that encompasses a wide range of general knowledge, providing ai systems with rich background information and contextual relationships. by integrating commonsense knowledge graphs into the training of ai systems, we can endow models with enhanced commonsense reasoning capabilities. examples of previously proposed commonsense knowledge graphs include conceptnet @xcite and atomic @xcite , which contain representations and inferences of numerous factual knowledge, thereby serving as valuable information sources for ai systems. however, these knowledge graphs often emphasize the representation of factual knowledge while neglecting the description of emotions. emotion is a multifaceted and complex psychological phenomenon that involves cognition, physiology, behavior, and the environment. the absence of knowledge in this domain leads to limited capabilities in understanding and reasoning about affective aspects. for example, as shown in figure 1 , a youtuber may need to shoot a video. if the individual lacks the necessary equipment, they may feel disappointed and seek solutions such as purchasing equipment. if the video editing is completed, they may sound delighted and feel grateful due to praise received. therefore, there is a need for the development of emotional knowledge graphs that can capture and represent the multifaceted nature of emotions. emotional commonsense knowledge graphs not only pay attention to factual knowledge but also emphasize the description and expression of emotions. compared to general commonsense knowledge graphs, emotional commonsense knowledge graphs place more emphasis on portraying emotional states, emotional triggers, and emotional reactions. this more nuanced representation of emotional knowledge will greatly facilitate artificial intelligence's understanding of emotional complexity, thereby enhancing its performance in areas such as human-computer interaction, natural language processing, and intelligent recommendation. in this work, we propose a dedicated emotional commonsense knowledge graph, ecok. based on research on the theory of emotional components and psychological analysis of emotions, we construct an emotional knowledge framework that focuses on seven related aspects of emotions centered around specific events: group, causes, bodily symptoms, feelings, expression, action tendencies, and advice. we extract and generate emotional knowledge from large language models, and use professionals to check the data to build a large-scale emotional commonsense knowledge graph. our final knowledge graph contains over 140k high-quality emotional reasoning knowledge. additionally, we train ecok using the comet framework @xcite and evaluate the performance of the generated commonsense knowledge and ecok in downstream tasks. in summary, our main contributions are as follows: ## related work commonsense knowledge graphs commonsense knowledge graphs can be referenced by machine learning models to handle a variety of challenging tasks, playing an important role in the field of natural language processing. among them, the conceptnet @xcite knowledge graph consists of 36 relations and mainly contains 3.4m taxonomic and lexical knowledge and physical commonsense knowledge. the atomic @xcite knowledge graph consists of 9 relations and 880k tuples, containing a large amount of social commonsense. the atomic 20 20 @xcite extends to 23 relations based on atomic, containing 1.33m tuples of daily reasoning knowledge. however, among these widely used knowledge graphs, there is no explicit focus on emotions. to extend the research on emotional commonsense knowledge graphs, we propose ecok, which can be better applied to natural language processingrelated tasks. psychological study of emotion emotion has been a fundamental area of inquiry within psychology, serving as a key factor in understanding human behavior, cognition, and social interactions @xcite . in the realm of emotional research, numerous theories and frameworks have emerged, each offering a unique perspective on the emotional landscape. for instance, the basic emotion theory posits that emotions are categorical, discrete, and universal, with a set of basic emotions such as happiness, sadness, anger, fear, surprise, and disgust serving as the foundation for more complex emotional experiences @xcite . on the other hand, the dimensional approach views emotions as varying along continuous dimensions, such as valence and arousal, allowing for a more nuanced representation of emotional states @xcite . the study of emotion in psychology has also examined the role of cognitive processes in emotional experiences. cognitive appraisal theories, for example, emphasize the importance of individuals' interpretations and evaluations of events in determining their emotional responses @xcite . these theories suggest that emotions arise from the cognitive evaluation of the personal significance and implications of events, highlighting the interplay between cognition and emotion. by incorporating these psychological perspectives, we can create more comprehensive and nuanced representations of emotional knowledge, enabling ai systems to better understand, reason about, and respond to the emotional dynamics of human experience. ## ecok framework emotion is a multi-dimensional and multi-level psychological phenomenon, involving cognition, physiology, behavior, and environment. firstly, the cognitive aspect of emotion includes our evaluation of things, attitude, and thinking style. different cognitive evaluations lead to different emotional reactions @xcite . for example, positive evaluation of the same event may lead to pleasant emotions, while negative evaluation may lead to sadness or anger. secondly, the physiological aspect of emotion involves the body's physiological response to emotions, such as accelerated heartbeat, elevated blood pressure, and sweating @xcite . these physiological responses are closely related to emotional states, but they are also influenced by individual differences and health status. additionally, the behavioral aspect of emotion manifests in facial expressions, body movements, and verbal expressions @xcite . different emotions have different behavioral manifestations, and these behavioral expressions can also affect the emotional reactions of others. finally, emotions are also influenced by environmental factors such as social and cultural background, interpersonal relationships, and specific situations @xcite . different cultures and social environments have different ways of expressing and understanding emotions, which requires us to have corresponding knowledge and abilities in different communication environments. in this field, how to define emotion has always been a thorny problem. the number of scientific definitions proposed has reached the point of almost incalculable. nowadays, what is generally accepted is the "component theories of emotion". in the framework of component process model, emotion is defined as a set of interrelated events @xcite , the components of an emotional event consist of the respective states of multiple subsystems and coordinated changes over time. generally, we do not have emotions for people or events that we do not care about. therefore, the key point of the definition of emotional components is that the events and consequences that cause them must be relevant to the main concerns of the organism. therefore, five components of emotion are derived: cognitive component (appraisal), neurophysiological component (bodily symptoms), motivational component (action tendencies), motor expression component (facial and vocal expression), and subjective feeling component (emotional experience) @xcite . based on the theoretical research on emotions mentioned above, we have ultimately identified seven relations for ecok: group, causes, bodily symptoms, feelings, expression, action tendencies, and advice. we will provide a detailed description of these relations below: group the classification of crowds that are most likely associated with the generation of certain emotions is outlined. for example, individuals likely to experience irritability due to staying up late include the demographic of middle-aged and young adults, students, as well as working professionals. causes describe the potential factors that led to the occurrence of the emotional descriptions event. for example, the reason for feeling irritable due to staying up late may stem from factors such as project deadlines and procrastination. bodily symptoms describe the physiological aspect of the emotional experience. for example, when experiencing irritability, physiological responses such as elevated blood pressure, increased heart rate, and digestive issues may manifest. feelings describe the subjective experience of emotional state once it has occurred. for example, following a night of staying up late, individuals often experience feelings of remorse. expression describe the responses and behavioral intentions accompanied by emotional states, such as facial expressions and vocal tone. for example, when individuals experience anxiety due to staying up late, manifestations such as verbal tension, irritability, and a furrowed brow commonly arise. action tendencies describe the motivations that arise from emotions. for example, when individuals experience anxiety, actions such as engaging in destructive behavior, shouting loudly may be observed. advice the recommendations on how to appropriately respond to emotions are outlined. note that the advice are generated based on the group of the target audience. for example, in the case of students experiencing anxiety, formulation of a well-structured study plan and seeking of support from peers or elders would be beneficial in alleviating anxiety. ## ecok construction in order to construct our emotional knowledge graph, we devise an end-to-end framework where events describing emotions are regarded as head entities in the graph, frame relations constitute edge type relations, with emotional attributes serving as tails in the triples @xcite . in figure 2 , we present an overview of our framework to construct ecok. the head entity of ecok is derived from two components: a) extraction from existing commonsense kgs, b) acquisition from open source social media dataset foot_0 . following this, we collected tail entities that related to emotion attributes from pretrained lms. subsequently, a final knowledge graph was formed through manual verification. ## conclusion in this work, we have successfully constructed ecok, a comprehensive and accurate emotional commonsense knowledge graph. ecok comprises over 140k high-quality emotional inference knowledge tuples, representing the complexity and diversity of emotions through seven distinct relationships: group, causes, bodily symptoms, feelings, expression, action tendencies, and advice. furthermore, we leverage ecok to build a knowledge generation model called comet-ecok, designed to facilitate more extensive knowledge generation. through rigorous evaluation, we have shown that incorporating emotional commonsense knowledge from ecok enhances the performance of knowledge generation, emotion recognition in conversations and casual emotion entailment tasks. these results validate the effectiveness of our ap-proach and its potential to revolutionize humanmachine interaction. in the future, we will continuously expand the emotional knowledge graph to explore more nuanced emotional representations and further enhance the capabilities of artificial intelligence systems in emotion-related tasks.