论文标题
使用上下文，情感和情感特征的讽刺检测框架
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features
论文作者
论文摘要
讽刺检测是一项必不可少的任务，可以帮助确定用户生成的数据中的实际情感，例如讨论论坛或推文。讽刺是语言表达的一种复杂形式，因为其表面含义通常与其内在，更深的含义相矛盾。这种不一致是讽刺的重要组成部分，但是，它使讽刺检测是一个艰巨的任务。在本文中，我们提出了一个模型，该模型结合了不同的特征，以捕获讽刺固有的不一致性。我们使用预先训练的变压器和CNN来捕获上下文特征，并使用预先训练的情绪检测和情感分析任务的变压器。我们的方法在来自社交网络平台和在线媒体的四个数据集上优于先前的最新结果。
Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.
