The most prominent subtask in emotion analysis is emotion classification; to assign a category to a textual unit, for instance a social media post. Many research questions from the social sciences do, however, not only require the detection of the emotion of an author of a post but to understand who is ascribed an emotion in text. This task is tackled by emotion role labeling which aims at extracting who is described in text to experience an emotion, why, and towards whom. This could, however, be considered overly sophisticated if the main question to answer is who feels which emotion. A targeted approach for such setup is to classify emotion experiencer mentions (aka "emoters") regarding the emotion they presumably perceive. This task is similar to named entity recognition of person names with the difference that not every mentioned entity name is an emoter. While, very recently, data with emoter annotations has been made available, no experiments have yet been performed to detect such mentions. With this paper, we provide baseline experiments to understand how challenging the task is. We further evaluate the impact on experiencer-specific emotion categorization and appraisal detection in a pipeline, when gold mentions are not available. We show that experiencer detection in text is a challenging task, with a precision of .82 and a recall of .56 (F1 =.66). These results motivate future work of jointly modeling emoter spans and emotion/appraisal predictions.

翻译：情感分析中最突出的子任务是情感分类，即为文本单元（例如社交媒体帖子）分配一个类别。然而，社会科学中的许多研究问题不仅需要检测帖子作者的情感，还需要理解文本中情感被归因于谁。这一任务由情感角色标注来应对，其目标是提取文本中描述的情感体验者、原因及情感指向对象。但若主要问题是“谁感受到何种情感”，这种方式可能显得过于复杂。针对该场景的一种定向方法是，将情感体验者提及（即“情感者”）按其假定感知的情感进行分类。该任务类似于人名命名实体识别，区别在于并非所有提及的实体名称都是情感者。尽管最近已有带有情感者标注的数据集可用，但尚无实验检测此类提及。本文通过基线实验探究该任务的挑战性，进一步评估在管线中缺乏黄金标准提及时，对体验者特定情感分类和评估检测的影响。结果表明，文本中的体验者检测是一项具有挑战性的任务，精确率为0.82，召回率为0.56（F1得分为0.66）。这些结果为未来联合建模情感者跨度与情感/评估预测的研究提供了动力。