Self-deprecating Humor Detection: A Machine Learning Approach

Kamal, Ashraf; Abulaish, Muhammad

doi:10.1007/978-981-15-6168-9_39

Cited by 11 publications

(16 citation statements)

References 12 publications

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“…Self-deprecating is one of the marketing campaign strategies to capture users' sentiments. Self-deprecating contents are mainly used for brand endorsement and product campaigning so that the sales volume can be scaled up [6]. Such contents enhance self-promotion marketing, and they improve the product-consumer relationship and create a place among the consumers.…”

Section: Why Self-deprecating Sarcasm?mentioning

confidence: 99%

“…Moreover, self-deprecating contents are structured in such a way that a brand accepts flaws without affecting its brand value. Besides OSNs, selfdeprecating contents are also seen in celebrities' interviews as well as in politicians' speeches [6]. Stieger et al [5] considered sarcasm as aggressive humor.…”

Section: Why Self-deprecating Sarcasm?mentioning

confidence: 99%

“…We ensured that crawled tweets are not shared or will not be shared in the future with any organization or third party/ person. During the experiment of this paper, we tried our level best to not violate Twitter's terms of service 5 and privacy policy 6 . Further, as per Twitter's content redistribution policy 7 , academic researchers are allowed to share an unlimited number of tweet ids only for peerreview or validation of research works.…”

Section: Ethical Aspectsmentioning

confidence: 99%

“…Effron [39] mentioned interjection as an overtly selfreferential, and it is explicitly present in most of the self-referential instances. Interjection is an important linguistic marker in sarcastic tweets as well [3,6,24,41]. If a pre-processed tweet matches with any one of the specific patterns that are given in Table 1, then it is added into the list of explicit self-referential tweets, Exp s .…”

Section: Self-referential Tweets Identificationmentioning

confidence: 99%

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CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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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.

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Section: Why Self-deprecating Sarcasm?mentioning

confidence: 99%

Section: Why Self-deprecating Sarcasm?mentioning

confidence: 99%

Section: Ethical Aspectsmentioning

confidence: 99%

Section: Self-referential Tweets Identificationmentioning

confidence: 99%

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CAT-BiGRU: Convolution and Attention with Bi-Directional Gated Recurrent Unit for Self-Deprecating Sarcasm Detection

Kamal

Abulaish

2021

Cogn Comput

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“…Interactions among the large user base generate massive data, which we can mine to extract valuable insights. In the existing literature, researchers have used the OSN datasets in many domains, such as sentiment analysis [1], [2], social bot detection [3], [4], sarcasm detection [5] and its different specializations [6], [7], [8], humor detection [9], recommendation system [10], [11], and rumor detection [12].…”

Section: Introductionmentioning

confidence: 99%

HCovBi-Caps: Hate Speech Detection Using Convolutional and Bi-Directional Gated Recurrent Unit With Capsule Network

Khan

Kamal²,

Fazil

et al. 2022

IEEE Access

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Adversaries and anti-social elements have exploited the rapid proliferation of computing technology and online social media in the form of novel security threats, such as fake profiles, hate speech, social bots, and rumors. The hate speech problem on online social networks (OSNs) is also widespread. The existing literature has machine learning approaches for hate speech detection on OSNs. However, the effectiveness of contextual information at different orientations is understudied. This study presents a novel Convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech. The proposed model is evaluated over two Twitter-based benchmark datasets -DS1(balanced) and DS2(unbalanced) with the best performance of 0.90, 0.80, and 0.84 respectively considering precision, recall, and f-score over unbalanced dataset. In terms of training and validation accuracy, the proposed model shows the best performance of 0.93 and 0.90, respectively, over the unbalanced dataset. In comparative evaluation, HCovBi-Caps demonstrates a significantly better performance than state-of-the-art approaches. In addition, HCovBi-Caps shows comparatively better performance over the unbalanced dataset. We also investigate the impact of different hyperparameters on the efficacy of HCovBi-Caps to ascertain the selection of their values. We observed that a higher value of routing iterations adversely affects the model performance, whereas a higher value of capsule dimension improves the performance.

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