  Datasets with induced emotion labels are scarce but of utmost importance for
many NLP tasks. We present a new, automated method for collecting texts along
with their induced reaction labels. The method exploits the online use of
reaction GIFs, which capture complex affective states. We show how to augment
the data with induced emotion and induced sentiment labels. We use our method
to create and publish ReactionGIF, a first-of-its-kind affective dataset of 30K
tweets. We provide baselines for three new tasks, including induced sentiment
prediction and multilabel classification of induced emotions. Our method and
dataset open new research opportunities in emotion detection and affective
computing.

Computation and Language;Computer Vision and Pattern Recognition;Machine Learning