Sarcasm detection using news headlines dataset

Abstract

Sarcasm has been an elusive concept for humans. Due to interesting linguistic properties, sarcasm detection has gained traction of the Natural Language Processing (NLP) research community in the past few years. However, the task of predicting sarcasm in a text remains a difficult one for machines as well, and there are limited insights into what makes a sentence sarcastic. Past studies in sarcasm detection either use large scale datasets collected using tag-based supervision or small scale manually annotated datasets. The former category of datasets are noisy in terms of labels and language, whereas the latter category of datasets do not have enough instances to train deep learning models reliably despite having high-quality labels. To overcome these shortcomings, we introduce a high-quality and relatively larger-scale dataset which is a collection of news headlines from a sarcastic news website and a real news website. We describe the unique aspects of our dataset and compare its various characteristics with other benchmark datasets in sarcasm detection domain. Furthermore, we produce insights into what constitute as sarcasm in a text using a Hybrid Neural Network architecture. First released in 2019, we dedicate a section on how the NLP research community has extensively relied upon our contributions to push the state of the art further in the sarcasm detection domain. Lastly, we make the dataset as well as framework implementation publicly available to facilitate continued research in this domain.