raquiba/Sarcasm_News_Headline

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. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets.

To overcome the limitations related to noise in Twitter datasets, this 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 HuffPost.

This new dataset has the following advantages over the existing Twitter datasets:

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 which are replies to other tweets, the news headlines we obtained are self-contained. This would help us in teasing apart the real sarcastic elements.

原始信息汇总

数据集概述

数据来源

• TheOnion: 提供讽刺性新闻标题，主要收集自“News in Brief”和“News in Photos”类别。
• HuffPost: 提供真实（非讽刺）新闻标题。

数据集优势

1. 语言质量高: 新闻标题由专业人士撰写，无拼写错误和非正式用法，减少了数据稀疏性，并增加了使用预训练嵌入的可能性。
2. 标签质量高: TheOnion专门发布讽刺新闻，因此标签噪声较低，质量高于Twitter数据集。
3. 内容自包含: 新闻标题独立，不依赖于其他推文的上下文，有助于识别真正的讽刺元素。