Malay Sarcasm Detection on Social Media: A Review, Taxonomy, and Future Directions

Suhaimi, Suziane Haslinda; Bakar, Nur Azaliah Abu; Azmi, Nurulhuda Firdaus Mohd

doi:10.1109/icitda55840.2022.9971158

Cited by 3 publications

(2 citation statements)

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“…The setback for this approach is that the process typically involves collecting and annotating a large amount of data, which can be time consuming and require significant effort and resources. Suhaimi et al [3] have listed several previous studies on sarcasm detection in languages other than English. It was found that almost all those on the list that focuses on studying sarcasm detection in languages other than English will develop their own datasets from scratch due to the lack of free resources.…”

Section: Related Workmentioning

confidence: 99%

“…These sarcasm detection features may include linguistic cues, such as lexical, pragmatic, hyperbole, pattern-based, syntactic, and metaphoric, as well as contextual factors, such as the speaker's tone, the audience, and the social and cultural context. Besides, different social media platforms may have unique communication norms and practices that can influence sarcasm based on click-based features, such as Facebook emotion reaction button, like button, and rating star [3]. By selecting the right features, a sarcasm detection model can improve its accuracy and generalizability across different contexts and cultures.…”

Section: Introductionmentioning

confidence: 99%

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Constructing and Analysing the MalaySarc Dataset: A Resource for Detecting and Understanding Sarcasm in Malay Language

Suhaimi,

AbuBakar,

Azmi

2023

World Congress on Electrical Engineering and Computer Systems and Science

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Social media platforms provide users with an efficient and effective way to interact with content without requiring lengthy or complex textual expressions. However, sarcasm in social media discourse has become a serious problem for researchers. Compared to English and several other main languages, the research on sarcasm and the accessibility of reference materials in the Malay language are still significantly lagging. Therefore, this study aims to develop a new dataset of Malay sarcasm detection by detailing each process step, from data collection to filtering to annotation. The dataset consists of two types of data: Facebook comments and its emotion reaction buttons, which include 6,325 non-sarcastic texts and 1,380 sarcastic texts. In addition, the descriptive analysis of this dataset was also conducted to determine the usage patterns of the main features of Malay sarcasm. The analysis shows that emoji is one of the features that play an essential role in determining sarcastic comments. Besides, there are pattern-based features based on the identification of high-frequency terms in the text. The resulting dataset provides diverse examples of sarcasm that consider the linguistic and cultural nuances of the language, thus improving the accuracy and reliability of identifying social media. The findings will aid future research in developing automatic Malay sarcasm detection models using machine learning.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%