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，即对文本单元（如社交媒体帖子）进行类别标注。然而，社会科学中的许多研究问题不仅需要检测文本作者的情感，还需要理解文本中被赋予情感的主体。这一任务由情感角色标注处理，旨在提取文本中描述为体验情感的主体、原因及情感指向对象。但若核心问题是"谁感受到何种情感"，这种方法可能过于复杂。针对这一场景的定向方法是：对情感体验者提及（即"情感表达者"）进行分类，识别其可能感知的情感。该任务类似于人物命名实体识别，区别在于并非所有提及的实体名称都是情感表达者。尽管最近已有标注情感表达者的数据可用，但尚未有检测此类提及的实验。本文通过基线实验探究该任务的挑战性，并进一步评估当黄金标准提及不可用时，情感表达者特定情感分类与评价检测在流水线中的影响。实验表明，文本中情感表达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值为0.66）。这些结果激励了未来对情感表达者跨度与情感/评价预测联合建模的研究。