Reactive Supervision: A New Method for Collecting Sarcasm Data

TL;DR

Reactive supervision is a novel data collection approach leveraging online conversation dynamics to efficiently gather large-scale sarcasm data, facilitating advancements in sarcasm detection and potentially benefiting other affective computing tasks.

Contribution

The paper introduces reactive supervision, a new method for collecting sarcasm data that overcomes limitations of existing techniques and provides a large, labeled dataset for research.

Findings

Created a large sarcasm dataset with contextual features

Demonstrated the method's adaptability to other affective domains

Enabled more effective sarcasm detection research

Abstract

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.