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）分配情感类别。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，还需要理解文本中谁被赋予了情感。这一任务通过情感角色标注来解决，其目标是提取文本中描述的情感体验者、情感产生的原因以及情感指向的对象。但如果主要问题是回答"谁感受到哪种情感"，这种方法可能显得过于复杂。针对此场景的一种定向方法是，对情感体验者的提及（即"情感者"）按照其可能感知的情感进行分类。该任务类似于针对人名的命名实体识别，区别在于并非每个提及的实体名称都是情感者。尽管近期已有带有情感者标注的数据集发布，但尚未开展检测此类提及的实验。本文通过基线实验来了解该任务的挑战性。我们进一步评估了在缺乏标准情感者提及的情况下，通过流水线方法进行体验者特定的情感分类和评价检测的影响。实验表明，文本中的体验者检测是一项具有挑战性的任务，其精确率为0.82、召回率为0.56（F1值为0.66）。这些结果激励了未来对情感者跨度与情感/评价预测联合建模的研究。