论文标题
一项关于Twitter上自动讽刺检测的调查
A Survey on Automated Sarcasm Detection on Twitter
论文作者
论文摘要
自动讽刺检测是计算机科学领域的增长领域。简短的短信越来越多地用于通信，尤其是在Twitter等社交媒体平台上。由于不足或缺少上下文，这些消息中未知的讽刺可以颠倒陈述的含义，从而导致混乱和交流失败。本文介绍了用于讽刺检测的各种当前方法，包括通过上下文检测，发布历史和机器学习模型。此外，可以观察到向深度学习方法的转变，这可能是由于使用具有诱导的模型而不是离散特征与变压器创新相结合的好处。
Automatic sarcasm detection is a growing field in computer science. Short text messages are increasingly used for communication, especially over social media platforms such as Twitter. Due to insufficient or missing context, unidentified sarcasm in these messages can invert the meaning of a statement, leading to confusion and communication failures. This paper covers a variety of current methods used for sarcasm detection, including detection by context, posting history and machine learning models. Additionally, a shift towards deep learning methods is observable, likely due to the benefit of using a model with induced instead of discrete features combined with the innovation of transformers.
