Abstract

Sentiment analysis and sarcasm detection are specialized areas in the field of information retrieval and natural language processing. Sentiment classification is closely correlated with sarcasm detection, where people usually adopt sarcasm to highlight their negative feeling. This paper proposes a novel multi-task deep neural networks for joint sarcasm detection and sentiment analysis (MT_SS). MT_SS train both tasks jointly using bidirectional gated recurrent unit with attention network module to obtain task-specific local feature representation while using convolutional neural networks to obtain global feature representation. The experiments on two datasets show that our proposed model outperforms the state-of-the-art approaches.

中文翻译：

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多任务深度神经网络用于联合讽刺检测和情感分析

摘要

情感分析和讽刺检测是信息检索和自然语言处理领域的专业领域。情绪分类与讽刺检测密切相关，人们通常会采用讽刺来强调自己的负面感觉。本文提出了一种新型的多任务深度神经网络，用于联合嘲讽和情感分析（MT_SS）。MT_SS使用双向门控递归单元和注意力网络模块共同训练两个任务，以获得特定于任务的局部特征表示，同时使用卷积神经网络获得全局特征表示。在两个数据集上的实验表明，我们提出的模型优于最新方法。