A rabic Emoji Sentiment Lexicon ( A rab- ESL ): A Comparison between A rabic and E uropean Emoji Sentiment Lexicons (2021)

Abstract: Emoji (the popular digital pictograms) are sometimes seen as a new kind of artificial and universally usable and consistent writing code. In spite of their assumed universality, there is some evidence that the sense of an emoji, specifically in regard to sentiment, may change from language to language and culture to culture. This paper investigates whether contextual emoji sentiment analysis is consistent across Arabic and European languages. To conduct this investigation, we, first, created the Arabic emoji sentiment lexicon (Arab-ESL). Then, we exploited an existing European emoji sentiment lexicon to compare the sentiment conveyed in each of the two families of language and culture (Arabic and European). The results show that the pairwise correlation between the two lexicons is consistent for emoji that represent, for instance, hearts, facial expressions, and body language. However, for a subset of emoji (those that represent objects, nature, symbols, and some human activities), there are large differences in the sentiment conveyed. More interestingly, an extremely high level of inconsistency has been shown with food emoji.

## introduction textual digital communication has become habitual these days among internet users. however, it has been argued that in such communication, the vital nonverbal cues are missing, which can potentially lead to ambiguity and misunderstanding @xcite . to address this issue, users tend to employ many surrogates, including non-standard punctuation, emoticons, and emoji. @xcite defined emoji as a form of developed punctuation (the way of encoding nonverbal prosody cues in writing systems) that supplements written language to facilitate the writers articulating their emotions in text-based communication. practically, emoji are actual icons in unicode transformation format (utf) that are a successor to emoticons (i.e., ascii character based) with more sophisticated rendering. these icons can represent facial expressions, body language, food, animals, places, and natural objects like flowers and trees. it has been observed that including emoticons, as well as emoji, in text not only helps the receivers to infer some contextual information, but it also eases understanding of the expressed sentiment @xcite . sentiment analysis, is the computational study of people's opinions, sentiments, emotions, and attitudes. it is one of the most active research areas in natural language processing (nlp) and is also extensively studied in data mining, web mining, and text mining @xcite . usually, it is a one dimensional measure from negative to positive and often it is quantized to just three values: negative, neutral or positive. sentiment analysis has become an important tool in classifying and interpreting text. as digital language is increasingly used across platforms @xcite , emoji are studied as an integral aspect of sentiment analysis in various research domains. this includes marketing @xcite , law @xcite , healthcare @xcite , food-related @xcite and addictivesubstances-related contents @xcite . the basis of many sentiment analysis approaches is the sentiment lexicon, with words and phrases classified as conveying positive or negative sentiments. several general-purpose word-based and phrase-based lexicons of subjectivity and sentiment have been constructed. emoji, likewise, can and should be viewed as an integral feature in the sentiment analysis process. however, most sentimentanalysis research has focused on emoji in languages other than arabic and, consequently, most of the resources developed (such as emoji sentiment lexicons and emoji-specific corpora) are non-arabic. thus, an analysis of arabic emoji sentiment us-age, provided as a result of this study, would be a valuable resource for research in arabic language analysis. in this work, we seek to investigate the differences in sentiment interpretation of emoji expressed in informal texts between two cultures (arabic and european), through the following: the rest of this paper is organized as follows. section 2 reviews related work upon which we build; section 3 presents the study's design; section 4 presents the results analysis and discussion. finally, in section 5 we draw conclusions from this work along with highlighting its limitations as well as some recommendations for future work. the usage patterns of emoji have been studied from several viewpoints such as relationships with specific topics @xcite , seasons (barbieri et al., 2018b), countries and regions @xcite as well as languages @xcite . here, we review some of the existing research that analyses emoji across cultures, and as sentiment features. ## study design kralj @xcite conducted a study, which considered context-sensitivity when analyzing the sentiment of emoji and texts, described above (i.e., in subsection 2.2). we followed their approach, with some additional steps, to construct our arabic emoji sentiment lexicon (arab-esl) as follows: ## results analysis and discussion here, we analyze our data patterns from an abductive perspective @xcite to form preliminary cultural emoji sentiment theories between european and arabic languages. as is shown in indeed, many sociological theories, proposing the universality of basic emotions @xcite , point out the tendency of emotional emoji to show high levels of similarity even between distinct cultures, like west and east @xcite . thus, emoji categories of facial expression, heart, and body language would be expected to be more convergent compared to other categories. on the other hand, the lowest correlations in emoji sentiment between the two lexicons occur in the food (-0.159), symbol (0.275), and human food emoji arab-esl euro-esl emoji name score label score label birthday cake 0.733333 positive 0.612745 positive honey pot 0.212766 positive 0.045455 neutral fish cake with swirl 0.575758 positive -0.593750 negative beer mug -0.206897 negative 0.492537 positive baby bottle -0.058824 neutral 0.454545 positive activities (0.275) categories. sentiment label agreement tests showed similar results with the same emoji categories. also, the cohen's kappa agreement coefficient resulted in κ = 0.0160 for food emoji, κ = 0.0581 for symbol emoji, and κ=-0.0556 for human activity emoji. in fact, this is not surprising. cultures are often instantiated in cuisines representing identities, religions, and dietary preferences @xcite @xcite . besides, symbols are often representative figurative characters of many specific cultural values @xcite . moreover, many human activities such as praying, working, or playing are affected by culture @xcite . as an example, ## conclusion, limitations and future work investigating emoji sentiment indications between cultures is one of the primary applications of an emoji sentiment lexicon. in this work, we exploited most of the existing arabic resources (covering all arabic dialects) to contribute an arabic emoji sentiment lexicon (arab-esl). as an application of the constructed lexicon, we compared the arab-esl with the already-existing similar european one. the results shows that cultures might share similar emoji sentiment indications. this occurs with emoji that represent common human behaviours, such as facial expressions, and body language, or basic emotions such as love and sadness. however, there are other emoji where their sentiments might be affected by a cultural-specific aspect, such as food, symbols, and human activities. these conclusions can be drawn from the results of employing two statistical methods: correlation coefficient and agreement coefficient. we are aware that there are variations, both geographically and within a specific culture (e,g., arabic or european) that makes it very difficult to completely rely on the results of such an investigation. however, we intend in the future to make the resulting emoji sentiment lexicon more fine grain for further, focused and detailed analytical studies of emoji within the arabic language. another limitation is that the sentiment dimensions are more than just a one dimensional-scale (i.e., negative to positive), and that this should be explored in the future. the emoji's eloquence allows them to be assigned more fine-grained sentiment labels like very negative and slightly negative, or more detailed emotional labels like anger, happiness, and sadness. in the future, this might help for an additional structuring of the emoji that can be obtained from correlations between their sentiments or emotions. for example, deriving different forms of facial expressions expressing happiness. also, the tests on sentiment classification were only based on simple statistics of sentiment scores of emoji in texts. future work can combine emoji sentiment scores within arabic semantics for more advanced sentiment analysis. besides, an emoji's textual context is crucial in determining the role of the emoji as a modifier of the meaning. more investigation should be applied with arabic content. finally, considering the promise of emoji in nlp application tasks, studies should discover the contribution of emoji in multi-modal and cross-lingual sentiment analysis along with transfer learning and deep learning tasks. moreover, it will be interesting to observe how the use of emoji by arabic users is growing, and whether their textual communication is increasingly being replaced by this pictorial language. also, the correlation between sentiment and meaning of emoji evolves over time. it might be important to explore the change in the meaning of controversial emoji, and how they are affected by the corresponding social processes.