Reactive Supervision: A New Method for Collecting Sarcasm Data
This addresses the data scarcity issue for researchers in affective computing, though it is incremental as it builds on existing data collection techniques.
The paper tackles the problem of collecting labeled data for sarcasm detection by introducing reactive supervision, a novel method that uses online conversation dynamics, and releases a large dataset of tweets with sarcasm labels and contextual features to advance research.
Sarcasm detection is an important task in affective computing, requiring large amounts of labeled data. We introduce reactive supervision, a novel data collection method that utilizes the dynamics of online conversations to overcome the limitations of existing data collection techniques. We use the new method to create and release a first-of-its-kind large dataset of tweets with sarcasm perspective labels and new contextual features. The dataset is expected to advance sarcasm detection research. Our method can be adapted to other affective computing domains, thus opening up new research opportunities.