  We 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.
