The most prominent tasks in emotion analysis are to assign emotions to texts and to understand how emotions manifest in language. An important observation for natural language processing is that emotions can be communicated implicitly by referring to events alone, appealing to an empathetic, intersubjective understanding of events, even without explicitly mentioning an emotion name. In psychology, the class of emotion theories known as appraisal theories aims at explaining the link between events and emotions. Appraisals can be formalized as variables that measure a cognitive evaluation by people living through an event that they consider relevant. They include the assessment if an event is novel, if the person considers themselves to be responsible, if it is in line with their own goals, and so forth. Such appraisals explain which emotions are developed based on an event, for example, that a novel situation can induce surprise or one with uncertain consequences could evoke fear. We analyze the suitability of appraisal theories for emotion analysis in text with the goal of understanding if appraisal concepts can reliably be reconstructed by annotators, if they can be predicted by text classifiers, and if appraisal concepts help to identify emotion categories. To achieve that, we compile a corpus by asking people to textually describe events that triggered particular emotions and to disclose their appraisals. Then, we ask readers to reconstruct emotions and appraisals from the text. This set-up allows us to measure if emotions and appraisals can be recovered purely from text and provides a human baseline to judge a model’s performance measures. Our comparison of text classification methods to human annotators shows that both can reliably detect emotions and appraisals with similar performance. Therefore, appraisals constitute an alternative computational emotion analysis paradigm and further improve the categorization of emotions in text with joint models.

情感分析中最突出的任务是将情感分配给文本并理解情感如何在语言中表现出来。对自然语言处理的一个重要观察是，即使没有明确提及情感名称，也可以通过单独提及事件来隐式传达情感，吸引对事件的移情、主体间理解。在心理学中，一类被称为评估理论的情绪理论旨在解释事件与情绪之间的联系。评估可以形式化为变量，用于衡量经历过他们认为相关的事件的人们的认知评估。它们包括评估事件是否新颖、此人是否认为自己有责任、是否符合他们自己的目标等等。这种评估解释了哪些情绪是基于事件而产生的，例如，一种新的情况会引起惊讶，或者具有不确定后果的情况会引起恐惧。我们分析了评价理论对文本情感分析的适用性，目的是了解评价概念是否可以由注释者可靠地重建，它们是否可以由文本分类器预测，以及评价概念是否有助于识别情感类别。为实现这一目标，我们通过要求人们用文字描述触发特定情绪的事件并披露他们的评价来编制语料库。然后，我们要求读者从文本中重建情感和评价。这种设置使我们能够衡量情绪和评价是否可以纯粹从文本中恢复，并提供人类基准来判断模型的性能指标。我们对文本分类方法与人类注释者的比较表明，两者都可以可靠地检测情绪和具有相似性能的评估。因此，评价构成了一种替代的计算情感分析范式，并进一步改进了具有联合模型的文本中的情感分类。