Many online comments on social media platforms are hateful, humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context. The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words & phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context.
翻译:社交媒体平台上的许多在线评论都是令人憎恶、幽默或讽刺的。 这些评论(特别是短评论)的讽刺性质改变了它们实际的隐含情感,导致现有情绪分析模型的误解。 已经做了许多研究,利用基于用户、专题和谈话的信息来发现文本中的讽刺性,但并没有做多少工作来利用判决间背景信息来发现同样情况。 本文提出了一个新的最先进的深层次学习结构,它使用一个新的双向间互动背景关注机制(Bi- ISCA)来捕捉在用户生成的短文本中发现讽刺性的相互依存性,仅使用谈话背景。 拟议的深层次学习模式表明能够捕捉出明确、隐含和背景不相容的言词和短语,从而导致援引讽刺。 Bi- ISCA 生成了两种广泛使用的基准数据基点的状态检测任务(Reddit和Twitter), 来捕捉取在用户生成的短视背景知识时, 使用我们当前版本背景关系中的最佳搜索机制。