Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches (2023)

Abstract: The term emotion analysis in text subsumes various natural language processing tasks which have in common the goal to enable computers to understand emotions. Most popular is emotion classification in which one or multiple emotions are assigned to a predefined textual unit. While such setting is appropriate for identifying the reader’s or author’s emotion, emotion role labeling adds the perspective of mentioned entities and extracts text spans that correspond to the emotion cause. The underlying emotion theories agree on one important point; that an emotion is caused by some internal or external event and comprises several subcomponents, including the subjective feeling and a cognitive evaluation. We therefore argue that emotions and events are related in two ways. (1) Emotions are events; and this perspective is the fundament in natural language processing for emotion role labeling. (2) Emotions are caused by events; a perspective that is made explicit with research how to incorporate psychological appraisal theories in NLP models to interpret events. These two research directions, role labeling and (event-focused) emotion classification, have by and large been tackled separately. In this paper, we contextualize both perspectives and discuss open research questions.

## introduction emotion analysis, however, goes beyond such analysis of propositional statements. to better understand what emotion analysis models are expected to do, it is worth reviewing emotion theories in psychology. there are many of them, with varying purposes and approaches, but most of them, if not all, agree on the aspect that emotions are caused by some event and come with a change of various subsystems, such as a change in motivation, a subjective perception, an expression, and bodily symptoms. another component is the evaluation of the causing event, sometimes even considered to constitute the emotion @xcite . the emotion also corresponds to an event itself, embedded in a context of other events, people, and objects. all components of such emotion events (cause, stances towards other involved people, opinions about objects) may be described along an explicit mention of an emotion name. any subset of them may appear in text, and may or may not be sufficient to reliably assign an emotion representation to the text author, a mentioned entity, or to a reader @xcite . this complexity has led to a set of various emotion analysis tasks in nlp, which we exemplify in an integrated manner in figure 1 . the most popular task is emotion prediction, either representing the writer's or the reader's emotion as a category, as valence/arousal values, or as appraisal vector (at the bottom of figure 1 , we will describe the underlying psychological theories in §2.1). adding the task of cause detection bridges to the role labeling setup (visualized in more completeness at the top). here, the emotion event is represented by the token span that represents the emotion experiencer, the cue, and the cause. emotion prediction focuses on understanding from text how events cause emotions, while role labeling focuses on understanding how emotions are represented as events themselves. ## open research tasks we have now discussed previous work in emotion analysis, appraisal-based approaches and role labeling. in the following, we will make a set of aspects explicit that, from our perspective, need future work. full emotion role labeling. several corpora exist now that have complex annotations of the emoter, their respective emotion stimuli, targets, and cue words; partially with sentence level annotations for the reader and writer in addition. modeling, however, focused on sequence labeling for subsets of the roles or sentence level classification. there are no attempts of full emotion graph prediction, despite that role prediction subtasks might benefit from being modeled jointly. there is also only little work on exploiting role information for emotion categorization on the sentence level, a potentially valuable approach for joint modeling of a structured prediction task with text classification. role labeling/stimulus detection with appraisal information. the work that has been performed to understand the interaction between role prediction and emotion categorization focused on predicting discrete emotion classes. however, stimuli often correspond to event descriptions and therefore are a straight-forward choice for further analysis with appraisal variables. also, understanding which event mentions in a text can function as an emotion stimulus could be supported with the help of appraisals. the detection of clauses or token sequences that correspond to emotion stimuli in context of appraisal-based interpretations therefore has potential to improve both subtasks. integration of other emotion models in role labeling. emotion categorization is typically one variable to be predicted in stimulus detection and role labeling approaches, either for a writer or for entities. an additionally interesting approach would be to integrate other emotion representations with role labeling. an interesting choice would be to create a corpus of valence/arousal values, assigned to specific entities and linked to stimuli. such approach comes with the general advantage of dimensional models, namely that emotion categories do not need to be predefined. ## conclusion with this paper, we discussed appraisal theorybased methods to interpret events, and how emotions can be represented as events with role labeling. we did that guided by our own two emotion analysis projects seat (structured multi-domain emotion analysis from text) and ceat (computational event evaluation based on appraisal theories for emotion analysis) which corresponded each to one of the two perspectives. these two fields have been approached mostly separately so far and the main goal of this paper is to make the research narrative behind both transparent, and, based on this, point out open research questions. such open tasks emerge from missing connections between the various goals in emotion analysis, but there are also other promising directions that we pointed out. we do not believe that this list is comprehensive, but hope that the aggregation of previous work and pointing at missing research helps interested researchers to identify the gaps they want to fill. emotion analysis is important to make computers aware of the concept, which is essential for natural communication. in addition, research in these fields helps to better understand how humans communicate, beyond building impactful computational systems. therefore, research in affective computing brings together psychology, linguistics, and nlp.