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Most research on emotion analysis from text focuses on the task of emotionclassification or emotion intensity regression. Fewer works address emotions asa phenomenon to be tackled with structured learning, which can be explained bythe lack of relevant datasets. We fill this gap by releasing a dataset of 5000English news headlines annotated via crowdsourcing with their associatedemotions, the corresponding emotion experiencers and textual cues, relatedemotion causes and targets, as well as the reader's perception of the emotionof the headline. This annotation task is comparably challenging, given thelarge number of classes and roles to be identified. We therefore propose amultiphase annotation procedure in which we first find relevant instances withemotional content and then annotate the more fine-grained aspects. Finally, wedevelop a baseline for the task of automatic prediction of semantic rolestructures and discuss the results. The corpus we release enables furtherresearch on emotion classification, emotion intensity prediction, emotion causedetection, and supports further qualitative studies.

GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception