Happy Dance, Slow Clap: Using Reaction GIFs to Predict Induced Affect on Twitter
This addresses the need for induced emotion data in NLP and affective computing, though it is incremental as it builds on existing emotion detection methods.
The authors tackled the scarcity of datasets with induced emotion labels by developing an automated method to collect texts with induced reaction labels using reaction GIFs, resulting in the creation of ReactionGIF, a dataset of 30K tweets with baselines for new tasks like induced sentiment prediction.
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.