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 to identify 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 NLP 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. We contributed to both directions with the projects SEAT (Structured Multi-Domain Emotion Analysis from Text) and CEAT (Computational Event Evaluation based on Appraisal Theories for Emotion Analysis), both funded by the German Research Foundation. In this paper, we consolidate the findings and discuss open research directions.

翻译：“情感分析”这一术语在文本分析领域涵盖了多项自然语言处理任务，其共同目标是使计算机能够理解情感。最常用的是情感分类任务，即为预定义的文本单元分配一种或多种情感。虽然这种设置适用于识别读者或作者的情感，但情感角色标注进一步引入了提及实体的视角，并提取与情感原因相对应的文本片段。基础情感理论在一个关键点上达成共识：情感由某些内部或外部事件引发，并包含多个子成分，包括主观感受和认知评估。因此，我们认为情感与事件之间存在双重关联：（1）情感本身即为事件，这一视角是自然语言处理中情感角色标注的基础；（2）情感由事件引发，这一视角在如何将心理学评估理论融入自然语言处理模型以解释事件的研究中得以明确体现。这两种研究方向——角色标注和（以事件为中心的）情感分类——在很大程度上被分别处理。我们通过德国研究基金会资助的SEAT（结构化多领域文本情感分析）和CEAT（基于评估理论的计算事件评估情感分析）两个项目，对这两个方向均做出了贡献。本文中，我们整合了相关研究成果，并讨论了尚待探索的研究方向。