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 point out open research questions.

翻译：文本中的情感分析这一术语涵盖了多种自然语言处理任务，其共同目标是使计算机能够理解情感。最流行的是情感分类，即将一种或多种情感分配给预定义的文本单位。虽然这种设置适用于识别读者或作者的情感，但情感角色标注增加了提及实体的视角，并提取对应于情感原因的文本片段。底层的情感理论在一个重要点上达成共识：情感由某种内部或外部事件引起，并包含若干子成分，包括主观感受和认知评价。因此，我们认为情感和事件之间存在两种关联方式。（1）情感即是事件；这一视角是自然语言处理中情感角色标注的基础。（2）情感由事件引起；这一视角通过研究如何将心理学评价理论融入自然语言处理模型以解释事件而得到明确体现。这两个研究方向——角色标注和（聚焦事件的）情感分类——在很大程度上是分别处理的。我们通过德国研究基金会资助的项目SEAT（结构化多领域文本情感分析）和CEAT（基于评价理论的计算性事件评估用于情感分析）对这两个方向均做出了贡献。在本文中，我们整合了研究发现，并指出了尚未解决的研究问题。