Automatic Emotion Experiencer Recognition
- URL: http://arxiv.org/abs/2305.16731v4
- Date: Thu, 27 Jul 2023 15:09:38 GMT
- Title: Automatic Emotion Experiencer Recognition
- Authors: Maximilian Wegge and Roman Klinger
- Abstract summary: We show that experiencer detection in text is a challenging task, with a precision of.82 and a recall of.56 (F1 =.66)
We show that experiencer detection in text is a challenging task, with a precision of.82 and a recall of.56 (F1 =.66)
- Score: 12.447379545167642
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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