Sarcasm detection has been a well-studied problem for the computational linguistic researchers. However, research related to different categories of sarcasm has still not gained much attention. Self-Deprecating Sarcasm (SDS) is a special category of sarcasm in which users apply sarcasm over themselves, and it is extensively used in social media platforms, mainly as an advertising tool for the brand endorsement, product campaign, and digital marketing with an aim to increase the sales volume. In this paper, we present a deep learning approach for detecting SDS on Twitter. We propose a novel Convolution and Attention with Bi-directional Gated Recurrent Unit (CAT-BiGRU) model, which consists of an input, embedding, convolutional, Bi-directional Gated Recurrent Unit (BiGRU), and two attention layers. The convolutional layer extracts SDS-based syntactic and semantic features from the embedding layer, BiGRU layer retrieves contextual information from the extracted features in both preceding and succeeding directions, and attention layers are used to retrieve SDS-based comprehensive context representation from the input texts. Finally, sigmoid function is employed to classify the input texts as a self-deprecating or non-self-deprecating sarcasm. Experiments are conducted over seven Twitter datasets to evaluate the proposed (CAT-BiGRU) model using standard evaluation metrics. The experimental results are impressive and significantly better than many neural network-based baselines and state-of-the-art methods. In this paper, we have highlighted biologically inspired and psychologically motivated basis of the proposed approach to examine its affective capabilities with respect to SenticNet. The efficacy of the proposed model is evaluated on two SenticNet-based sentic computing resources—Amazon word embedding and AffectiveSpace. Based on the experimental results, we conclude that deep learning-based approaches have potential to detect SDS in social media texts accurately.

讽刺检测对于计算语言学研究人员来说是一个经过充分研究的问题。然而，与不同类别的讽刺相关的研究仍未引起足够的重视。自嘲自嘲（SDS）是一类特殊的嘲讽，用户在其中使用嘲讽，广泛用于社交媒体平台，主要用作品牌认可，产品推广和数字营销的广告工具增加销量。在本文中，我们提出了一种用于在Twitter上检测SDS的深度学习方法。我们提出了一种新颖的具有双向门控递归单元的卷积和注意力（CAT-BiGRU）模型，它由输入，嵌入，卷积，双向门控循环单元（BiGRU）和两个注意层组成。卷积层从嵌入层中提取基于SDS的句法和语义特征，BiGRU层从提取的特征中沿前后方向检索上下文信息，注意力层用于从输入文本中检索基于SDS的综合上下文表示。最后，乙状结肠函数用于将输入文本分类为自嘲或非自嘲讽刺。在七个Twitter数据集上进行了实验，以使用标准评估指标评估提议的（CAT-BiGRU）模型。实验结果令人印象深刻，并且比许多基于神经网络的基线和最新技术要好得多。在本文中，我们重点介绍了该方法的生物学启发和心理动机基础，以检验其对SenticNet的情感能力。在两个基于SenticNet的感知计算资源-Amazon word嵌入和AffectiveSpace上评估了该模型的有效性。根据实验结果，我们得出结论，基于深度学习的方法有潜力准确检测社交媒体文本中的SDS。