Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as structured phenomena , which can be explained by the lack of relevant datasets and methods . We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues,  emotion causes and targets, as well as the reader's perception and emotion of the headline. We propose a multiphase annotation procedure which leads to high quality annotations on such a task via crowdsourcing . Finally, we develop a baseline for the task of automatic prediction of  structures and discuss  results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.
Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning , which can be explained by the lack of relevant datasets . We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader's perception of the emotion of the headline. This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore propose a multiphase annotation procedure in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects . Finally, we develop a baseline for the task of automatic prediction of semantic role structures and discuss the results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.
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structured phenomena
a phenomenon to be tackled with structured learning
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and methods
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dominant emotions,
associated emotions, the corresponding
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related
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and
of the
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We
This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore
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which leads to high quality annotations on such a task via crowdsourcing
in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects
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semantic role
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the