Multi-Emotion Estimation in Narratives from Crowdsourced Annotations

Abstract

Emotion annotations are important metadata for narrative texts in digital libraries. Such annotations are necessary for automatic text-to-speech conversion of narratives and affective education support and can be used as training data for machine learning algorithms to train automatic emotion detectors. However, obtaining high-quality emotion annotations is a challenging problem because it is usually expensive and time-consuming due to the subjectivity of emotion. Moreover, due to the multiplicity of "emotion", emotion annotations more naturally fit the paradigm of multi-label classification than that of multi-class classification since one instance (such as a sentence) may evoke a combination of multiple emotion categories. We thus investigated ways to obtain a set of high-quality emotion annotations ({instance, multi-emotion} paired data) from variable-quality crowdsourced annotations. A common quality control strategy for crowdsourced labeling tasks is to aggregate the responses provided by multiple annotators to produce a reliable annotation. Given that the categories of "emotion" have characteristics different from those of other kinds of labels, we propose incorporating domain-specific information of emotional consistencies across instances and contextual cues among emotion categories into the aggregation process. Experimental results demonstrate that, from a limited number of crowdsourced annotations, the proposed models enable gold standards to be more effectively estimated than the majority vote and the original domain-independent model.