  This paper examines the task of detecting intensity of emotion from text. We
create the first datasets of tweets annotated for anger, fear, joy, and sadness
intensities. We use a technique called best--worst scaling (BWS) that improves
annotation consistency and obtains reliable fine-grained scores. We show that
emotion-word hashtags often impact emotion intensity, usually conveying a more
intense emotion. Finally, we create a benchmark regression system and conduct
experiments to determine: which features are useful for detecting emotion
intensity, and, the extent to which two emotions are similar in terms of how
they manifest in language.
