Sarcasm Detection: A Comparative Study

Yaghoobian, Hamed; Arabnia, Hamid R.; Rasheed, Khaled

doi:10.48550/arxiv.2107.02276

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Moreover, we mined different types of statistical and lexical-based features that were previously applied in irony detection. Additional text features can improve the detection of sarcasm in many related tasks (Hernández-Farías et al, 2015;Yaghoobian et al, 2021). All the text features are simply added to the preprocessed tweets using the splitting token "< /s > < /s >".…”

Section: Feature Miningmentioning

confidence: 99%

“…Sarcasm detection can be considered a particular sentiment analysis task, applied to detect texts that are intended to use some exaggeration, understatement, or rhetoric content to express criticism or praise for people or events. Many researchers have conducted different deep learning methods (Poria et al, 2016;Kumar et al, 2020;Zhang et al, 2019), traditional machine learning method (Buschmeier et al, 2014;Hernández-Farías et al, 2015;Yaghoobian et al, 2021), and big data approaches (Bharti et al, 2016;Sarsam et al, 2020;Ortega-Bueno et al, 2019) to improve the accuracy of irony or sarcasm autodetection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets

Yuan¹,

Mengyuan²,

Jiang³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes the systematic approach applied in "SemEval-2022 Task 6 (iSarcas-mEval) : Intended Sarcasm Detection in English and Arabic". In particular, we illustrate the proposed system in detail for SubTask-A about determining a given text as sarcastic or non-sarcastic in English. We start with the training data from the officially released data and then experiment with different combinations of public datasets to improve the model generalization. Additional experiments conducted on the task demonstrate our strategies are effective in completing the task. Different transformerbased language models, as well as some popular plug-and-play proirs, are mixed into our system to enhance the model's robustness. Furthermore, statistical and lexical-based text features are mined to improve the accuracy of the sarcasm detection. Our final submission achieves an F1-score for the sarcastic class of 0.6052 on the official test set (the top 1 of the 43 teams in "SubTask-A-English" on the leaderboard).

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Section: Feature Miningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets

Yuan¹,

Mengyuan²,

Jiang³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

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“…Text detection is one of the big challenges of NLP which still has many limitations to work with, especially for the Arabic language when compared to English. There are many types of text detection such as prediction of human behavior [ 10 ], hate speech detection [ 11 ], exploring halal tourism [ 12 ], gender detection [ 13 ], misogyny detection [ 14 ], sarcasm detection [ 15 ], detection and classification of psychopathic personality [ 16 ], fake news detection [ 17 ], and detection of dialectal in the Arabic language [ 18 ]. This study aims to design a model for automatically detecting misogyny and sarcasm using machine learning (ML) and deep learning (DL) methods with different benchmark datasets [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

Muaad

Hanumanthappa

Benifa

et al. 2022

Computational Intelligence and Neuroscience

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Social media networking is a prominent topic in real life, particularly at the current moment. The impact of comments has been investigated in several studies. Twitter, Facebook, and Instagram are just a few of the social media networks that are used to broadcast different news worldwide. In this paper, a comprehensive AI-based study is presented to automatically detect the Arabic text misogyny and sarcasm in binary and multiclass scenarios. The key of the proposed AI approach is to distinguish various topics of misogyny and sarcasm from Arabic tweets in social media networks. A comprehensive study is achieved for detecting both misogyny and sarcasm via adopting seven state-of-the-art NLP classifiers: ARABERT, PAC, LRC, RFC, LSVC, DTC, and KNNC. To fine tune, validate, and evaluate all of these techniques, two Arabic tweets datasets (i.e., misogyny and Abu Farah datasets) are used. For the experimental study, two scenarios are proposed for each case study (misogyny or sarcasm): binary and multiclass problems. For misogyny detection, the best accuracy is achieved using the AraBERT classifier with 91.0% for binary classification scenario and 89.0% for the multiclass scenario. For sarcasm detection, the best accuracy is achieved using the AraBERT as well with 88% for binary classification scenario and 77.0% for the multiclass scenario. The proposed method appears to be effective in detecting misogyny and sarcasm in social media platforms with suggesting AraBERT as a superior state-of-the-art deep learning classifier.

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“…Due to the inherent metaphorical nature and subtle sentimental expression of this particular form of language expression. The detection task related to this kind of text, which is a negative expression of a positive emotion or the positive expression of negative emotion, is extremely difficult for machines (Yaghoobian et al, 2021). This sarcasm data also weakens the detection modules that are widespread in our society (Maynard and Greenwood, 2014).…”

Section: Introductionmentioning

confidence: 99%

PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm

Du¹,

Hu²,

Jin³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes the method we utilized in the SemEval-2022 Task 6 iSarcasmEval: Intended Sarcasm Detection In English and Arabic. Our system has achieved 1st in Sub-taskB, which is to identify the categories of intended sarcasm. The proposed system integrates multiple BERT-based, RoBERTa-based and BERTweet-based models with finetuning. In this task, our contribution is listed as follow: 1) we reveal several large pre-trained models' performance on tasks coping with the tweetlike text. 2) Our methods prove that we can still achieve excellent results in this particular task without a complex classifier adopting some proper training method. 3) we found there is a hierarchical relationship of sarcasm types in this task.

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Sarcasm Detection: A Comparative Study

Cited by 5 publications

References 26 publications

stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets

stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets

Artificial Intelligence-Based Approach for Misogyny and Sarcasm Detection from Arabic Texts

PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm

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