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

Kamal, Ashraf; Abulaish, Muhammad

doi:10.1007/s12559-021-09821-0

Cited by 29 publications

(15 citation statements)

References 47 publications

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“…The attention system is implemented afterward to consider the expression habits of users. Kamal and Abulaish [16] modeled a new Convolutional and Attention with Bi-directional GRU (CAT-BiGRU) method, which has an input layer, embedded layer, convolution layer, Bi-directional GRU (BiGRU) layer, and two attention layers [17]. The convolution layer extracts SDS-related semantic and syntactic characteristics from the embedded layer; the BiGRU layer retrieves contextual data from the features, which are extracted in succeeding and preceding directions; and the attention layers retrieve SDS-related complete context representation from the input text [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

et al. 2022

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Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency–inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques.

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Section: Literature Reviewmentioning

confidence: 99%

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

et al. 2022

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“…In the second level, the polarity between the sentiment semantic features and all the words in the sentence is captured to detect sarcasm by combining the LSTM and CNN networks. Later, a more complex framework was proposed in [45], in which the researchers proposed a Self-Deprecating Sarcasm (SDS) framework that incorporates GloVe embedding, CNN to extract features, bidirectional gated recurrent unit (BiGRU) to extract context information that would be useful for SDS classification, and two attention layers to assign higher weights to SDS-identified sarcastic words.…”

Section: Dl-based Approachesmentioning

confidence: 99%

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Alqahtani¹,

Alhenaki²,

Alsheddi³

2023

IJACSA

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Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others. Sentiment analysis is a process of uncovering the subjective information from a text. Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis. Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people's opinions about certain topics, news, and products. This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users. As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts. Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers. However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text. In addition, very few reviews have been conducted on the research in this area. Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022. Several databases were extensively searched, and 30 articles that met the criteria were included. The selected articles were reviewed based on their approaches, datasets, and evaluation metrics. The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively. Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.

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“…Du et al [ 21 ] designed a dual-channel structure to effectively analyze the semantics of the target text and its sentimental context. Kamal and Abulaish [ 22 ] proposed a convolution and attention model with a bi-directional gated recurrent unit to detect self-deprecating sarcasm, where syntactic and semantic features and a comprehensive context representation were captured. Lou et al [ 12 ] employed a graph convolutional network based on affective information and syntactical information from sentences in order to capture long-range incongruity patterns and inconsistent expressions in the context for sarcasm detection.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network

Hao

Yao

Shi

et al. 2023

Entropy

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Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features by using lexicon, n-gram, and pragmatic feature-based models. However, these methods ignore the diverse contextual clues that could provide more evidence of the sarcastic nature of sentences. In this work, we propose a Contextual Sarcasm Detection Model (CSDM) by modeling enhanced semantic representations with user profiling and forum topic information, where context-aware attention and a user-forum fusion network are used to obtain diverse representations from distinct aspects. In particular, we employ a Bi-LSTM encoder with context-aware attention to obtain a refined comment representation by capturing sentence composition information and the corresponding context situations. Then, we employ a user-forum fusion network to obtain the comprehensive context representation by capturing the corresponding sarcastic tendencies of the user and the background knowledge about the comments. Our proposed method achieves values of 0.69, 0.70, and 0.83 in terms of accuracy on the Main balanced, Pol balanced and Pol imbalanced datasets, respectively. The experimental results on a large Reddit corpus, SARC, demonstrate that our proposed method achieves a significant performance improvement over state-of-art textual sarcasm detection methods.

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

Cited by 29 publications

References 47 publications

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks

Text-based Sarcasm Detection on Social Networks: A Systematic Review

Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network

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