Abstract
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag-based supervision but such datasets are noisy in terms of labels and language. To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from Huff Post. Sarcasm Detection on social media platform. The dataset is collected from two news websites, theonion.com and huffingtonpost.com. Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings. Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets. Unlike tweets that reply to other tweets, the news headlines obtained are self-contained.
Highlights
The modern world can be described as a data-driven environment
The Research is about caustic remarks, assertions, and pronouncements found in articles/posts on social media sites, as the title suggests
Our ensemble technique beats state-of-the-art sarcasm detection methods and popular unbalanced classification methods, according to experimental results
Summary
The modern world can be described as a data-driven environment. The amount of data generated by a single network device has increased exponentially. They ignore the imbalance between sarcastic and non-sarcastic samples in real applications and do not employ explicit features to detect sarcasm. We start by looking at the properties of sarcastic sentences and present a collection of features that can be used to detect sarcasm in social media.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
