WASSA-2017 shared task on emotion intensity

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Journal titleComputer Science
Article numberarXiv:1708.03700
AbstractWe present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best--worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.
Publication date
PublisherCornell University Library
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedNo
NPARC number23002132
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Record identifier4230a39d-5839-4465-9212-8cb2163b9037
Record created2017-08-24
Record modified2017-08-24
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