Emotions are a central aspect of communication. Consequently, emotion analysis (EA) is a rapidly growing field in natural language processing (NLP). However, there is no consensus on scope, direction, or methods. In this paper, we conduct a thorough review of 154 relevant NLP publications from the last decade. Based on this review, we address four different questions: (1) How are EA tasks defined in NLP? (2) What are the most prominent emotion frameworks and which emotions are modeled? (3) Is the subjectivity of emotions considered in terms of demographics and cultural factors? and (4) What are the primary NLP applications for EA? We take stock of trends in EA and tasks, emotion frameworks used, existing datasets, methods, and applications. We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields. Our work will enable more focused research into EA and a more holistic approach to modeling emotions in NLP.

翻译：情感是沟通的核心方面。因此，情感分析（EA）是自然语言处理（NLP）中一个快速发展的领域。然而，目前在其范围、方向或方法上尚未达成共识。本文对过去十年间154篇相关NLP出版物进行了全面回顾。基于此回顾，我们探讨了四个不同的问题：（1）NLP中如何定义EA任务？（2）最突出的情感框架有哪些？建模了哪些情感？（3）是否在人口统计学和文化因素层面考虑了情感的主观性？（4）EA的主要NLP应用是什么？我们评估了EA及其任务、所用情感框架、现有数据集、方法和应用的趋势。随后，我们讨论了四个空白：（1）缺乏人口统计学和文化维度，未能解释情感感知的差异，反而假设情感是以相同方式普遍体验的；（2）两种主要情感理论中的情感类别与任务匹配度较差；（3）缺乏标准化的EA术语阻碍了空白识别、比较及未来目标的设定；（4）缺乏跨学科研究使得EA与其他领域的见解相隔离。我们的工作将推动对EA进行更聚焦的研究，并促进在NLP中以更全面的方法建模情感。