A r S arcas M oji Dataset: The Emoji Sentiment Roles in A rabic Ironic Contexts (2023)

Abstract: In digital communication, emoji are essential in decoding nuances such as irony, sarcasm, and humour. However, their incorporation in Arabic natural language processing (NLP) has been cautious because of the perceived complexities of the Arabic language. This paper introduces ArSarcasMoji, a dataset of 24,630 emoji-augmented texts, with 17. 5% that shows irony. Through our analysis, we highlight specific emoji patterns paired with sentiment roles that denote irony in Arabic texts. The research counters prevailing notions, emphasising the importance of emoji’s role in understanding Arabic textual irony, and addresses their potential for accurate irony detection in Arabic digital content.

## introduction irony, sarcasm, and humour are intricate forms of expression that craft messages in nuanced, playful, or mock-serious tones. although they might intersect in their application, each has a unique meaning and utility. irony portrays situations or statements contrary to what is expected, manifesting verbally or situationally @xcite . sarcasm, a specific facet of irony, employs language with a sharp, often bitter tone to mock or critique, often with exaggerated emphasis @xcite . within the arabic social media sphere, sarcasm often serves as a tool for social commentary or political satire, acting as a medium to challenge authority and examine social norms and values @xcite @xcite . humour, while encompassing elements of irony, specifically denotes the ability to evoke amusement or laughter @xcite . it manifests itself in diverse formats, from jokes and puns to witty remarks. in arabic digital communication, humour is praised for its prowess in communicating intricate emotions and fostering positive sentiments in a light-hearted and engag-ing manner @xcite . in today's digital age, emoji have emerged as powerful tools in the linguistic landscape. these are small digital icons that are used to convey emotions or ideas. although once dismissed as mere decorative elements, they are now acknowledged for their crucial role in amplifying and clarifying textual sentiments, moods, and intentions @xcite @xcite . by providing much needed context, especially on platforms where vocal tonalities and facial cues are absent, emoji enrich the emotional depth of a message. they have become instrumental in detecting nuances such as sarcasm and humour, underscoring their importance in contemporary studies of natural language processing (nlp) @xcite @xcite . although the importance of emoji in the deciphering of textual context is undeniable, there exists a contrasting trend in arabic nlp. given the inherent intricacies of the arabic language, characterised by its rich morphological structures and multifaceted semantics, many researchers opt to exclude emoji when analysing irony. this practise stems from the belief that emoji could introduce an additional layer of complexity, potentially diverting the focus from the linguistic nuances unique to arabic. as such, despite the global trend of integrating emoji into textual analysis, there is a cautious approach within arabic nlp circles, underscoring the challenges and distinctiveness of the arabic linguistic landscape. in response to this observed gap in arabic nlp research, this paper presents the community with a unique dataset (arsarcasmoji) consisting of 24,630 short texts enriched with emoji (4,320 are ironic and 20,310 are not) 1 . our exploration goes beyond conventional analyses to illustrate the cru-cial role these emoji play in discerning sarcasm and humour within the texts. through careful analysis, we show that emoji are not just additional symbols, which goes against common beliefs. instead, they often hold the key to unmasking the ironic intent behind a statement. in this paper, we test this claim as follows. for a text to be labelled as ironic, it must feature a specific emoji pattern with distinct sentiment roles. accordingly, our research focuses on three main questions: providing arsarcasmoji dataset along with this analysis underscores the indispensable value of emoji in enhancing our understanding of textual irony in the arabic informal social media language. the remainder of this paper is organised as follows. section 2 reviews related work; section 3 presents the study methodology; section 4 presents the analysis of the dataset. finally, in section 5 we draw conclusions from this work along with some recommendations for future work as well as highlight its limitations. ## related work sarcasm and humour, two intricate linguistic phenomena, have garnered significant attention in the field of nlp. their detection in textual data is paramount for improved sentiment analysis, better content recommendation, and the promotion of nuanced human-machine interactions. in the fast-evolving domain of nlp, the challenge of irony detection in arabic stands out, given the language's diverse dialects and rich linguistic intricacies. sentiment analysis has long wrestled with this complexity, primarily due to the nuances of spotting indirect phrasing that often conveys meanings contrary to their overt expressions. taking steps in this area, abu farha and magdy (2020) introduced the arsarcasm dataset. derived from reannotating existing arabic sentiment datasets, arsarcasm features 10,547 tweets with 16% labelled sarcastic. the research highlighted the subjective challenges of sentiment annotation and the diminished efficacy of modern sentiment analysis systems when confronted with sarcasm. furthermore, a bilstm-based model they developed for sarcasm detection achieved an f1 score of 0.46, underscoring the task's complexity. however, a notable limitation in their study was the neglect of the roles of emoji, which often play a pivotal role in conveying and deciphering sarcasm in textual communications. in the following step, a research study by @xcite unveiled the sa'7r, a saudispecific irony dataset derived from 19,810 tweets (8,089 of which were labelled ironic). in their endeavour, they trained an array of classifiers, encompassing machine learning models like knn, lr, svm, and nb, as well as deep learning contenders such as bilstm and arabert. among these, the svm algorithm emerged as the most proficient in traditional techniques, boasting an accuracy of 0.68, while in the deep learning arena, arabert led with an impressive 0.71 accuracy. this establishes arabert as a primary tool for discerning irony within the nuances of saudi dialects. however, the study did not highlight the feature of emoji in this task. in parallel, @xcite paved the way with a distinctive dataset of 10,039 tweets, covering various arabic dialects and modern standard arabic, meticulously annotated for humourous and non-humourous content. with rigorous pre-processing steps, including arabic normalisation and the pruning of unrelated text, the camelbert-da model achieved an accuracy of 72.11%. despite that, a critical gap was the dataset's exclusion of emoji. in today's digital era, emoji are instrumental in relaying sentiments, particularly in the realm of irony. this oversight in the already-existing arabic datasets might hint at the datasets' potential limitation in truly capturing the intricacies of contemporary arabic sarcasm and humour, marking an area ripe for further research and development. @xcite explored the central role of emoji in irony detection in english texts. observing the under-representation of emoji in ironic tweets in existing english datasets, they proposed an automated pipeline for more balanced data. their findings highlighted how emoji can transform text sentiment, converting straightforward statements into ironic ones. they augmented the datasets, making the models attuned to text and emoji signals, and, their research posited an innovative approach towards understanding the sentiment implications of emoji, particularly within arabic textual frameworks. the findings reaffirm that an emoji's sentiment role can oscillate among three paradigms: negative, neutral, or positive. specifically, an emoji can function as an emphasizer, indicator, mitigator, reverser, or trigger of negative or positive sentiments within a textual context. there was also an intriguing proposition that certain emoji can exert a neutral effect essentially leaving the text with a neutral sentiment. in distilling the gamut of roles that emoji can play in sentiment analysis, this research provided invaluable insights for scholars seeking to understand the nuanced interplay of emoji and text. expanding on the emoji-sentiment-roles model by @xcite , we formulated an irony classification technique that allowed the creation of the arsarcasmoji dataset. details are as follows. ## arsarcasmoji dataset analysis to the best of our knowledge, arsarcasmoji is the premier dataset in arabic dedicated to the analysis of emoji, with a particular focus on ironic behaviours. this dataset encompasses 24,630 arabic texts with emoji, as well as parallel versions of these texts devoid of emoji. both categories of texts-with and without emoji-have undergone sentiment annotation. moreover, the sentiment roles of emoji patterns and the ironic demeanour of each emoji-inclusive text are clearly defined. delving into the ironic nature of emoji use, the dataset boasts 4,320 ironic texts (3,573 classified as sarcastic and 747 as humorous), in contrast to 20,310 non-ironic texts. the sentiment roles of the emoji patterns, juxtaposed with their irony-induced effects, are visualized in figure 1 . for a more illustrative insight, figure 2 presents samples of both sarcastic and humorous texts. a more in-depth exploration of the dataset is provided subsequently. ## conclusion and future work this study ventured into the relatively uncharted territory of emoji-augmented arabic texts to discern nuances like humour and sarcasm. with the introduction of the arsarcasmoji dataset, we have taken a pivotal step towards understanding the interplay of emoji patterns and sentiment roles in arabic digital content. our investigation has revealed that contrary to established beliefs, emoji play an indispensable role in accurately decoding arabic textual irony. their integration does not simply add another layer of complexity but rather serves as an essential tool to unmask the true intent behind statements. several avenues beckon exploration in the realm of arabic nlp. one promising direction is to delve deeper into the multi-faceted semantics of the arabic language and how emoji can further contribute to understanding other linguistic nuances, beyond irony. another avenue would be to expand our dataset by incorporating different social media platforms, thereby ensuring a holistic understanding of emoji usage across the digital landscape. furthermore, a comparative study between emojiaugmented arabic texts and those of other languages might shed light on cultural nuances and their implications in nlp. lastly, it would be insightful to develop machine learning models that can leverage the rich insights offered by the ar-sarcasmoji dataset for automated irony detection, sentiment analysis, and beyond.