Emoji Sentiment Roles for Sentiment Analysis: A Case Study in A rabic Texts (2022)

Abstract: Emoji (digital pictograms) are crucial features for textual sentiment analysis. However, analysing the sentiment roles of emoji is very complex. This is due to its dependency on different factors, such as textual context, cultural perspective, interlocutor’s personal traits, interlocutors’ relationships or a platforms’ functional features. This work introduces an approach to analysing the sentiment effects of emoji as textual features. Using an Arabic dataset as a benchmark, our results confirm the borrowed argument that each emoji has three different norms of sentiment role (negative, neutral or positive). Therefore, an emoji can play different sentiment roles depending upon the context. It can behave as an emphasizer, an indicator, a mitigator, a reverser or a trigger of either negative or positive sentiment within a text. In addition, an emoji may have a neutral effect (i.e., no effect) on the sentiment of the text.

## introduction human social interaction consists not only of verbal exchanges, but also of non-verbal signals such as head-nods, facial expressions, gestures, posture, eye-movements or tone of voice. in text-based communication, it has been argued that many of these nonverbal cues are missed, which potentially makes the communication ambiguous and leads to misunderstandings @xcite . to mitigate this issue in textual messages, people tend to use many kinds of surrogates, such as emoticons (e.g. ":)" or ":(" ), and emoji (like and ). @xcite categorized these nonverbal cues in text-based communication into five types: vocal spelling, lexical surrogates, spatial arrays, manipulation of grammatical markers, and minus features. among these, emoticons and emoji are considered as examples of spatial arrays, that make a significant contribution to the interpretation of the textual contents' sentiment. sentiment analysis is used to discover opinions, emotions and attitudes in textual contents. accordingly, @xcite defined emoji as a form of developed punctuation (the way of encoding nonverbal prosody cues in writing systems) that supplements the written language to facilitate the writer's articulation of their emotions in text-based communication. also, @xcite considered the use of emoji to be understood as analogically encoded symbols that are sensitive to a senderreceiver relationship, and that are fully integrated with the accompanying words (i.e., visible acts of meaning @xcite ). the view adopted in this work is that the visual representation of an emoji is a feature that influences the writer's choice of emoji @xcite . as a result, it can affect wider stretches of text and so the emoji often tend to co-occur with 'negative' ('bad', 'unpleasant'), neutral ('non-emotional', 'mixedemotional'), or 'positive' ('good', 'pleasant') collocates. these collocates can be either words or other emoji. this is similar to what discourse analysts call the "contextual valence shifters" @xcite . contextual valence shifters are factors which assess a writer's attitude towards an event being described. this assessment relies on the lexical choice of the writer (i.e., the roles of the chosen words in the expressed texts), and the organization of the text. for example, @xcite state that words often shift the valence of evaluative terms through their presuppositions. the adverb "barely", for instance, when it comes with the word "sufficient" changes it sentiment from positive "sufficient" into negative "barely sufficient". the later presupposes that better was expected. thus, in order to discover the sentiment roles of emoji within the body of a text, we need to investigate their general emoji-sentiment co-existence behaviors. to this end, we started our study by in-vestigating all of the possible sentiment states that might occur when comparing the same text with and without emoji. accordingly, we defined a set of emoji roles in the sentiment analysis of the accompanying texts. then, we analyzed the results to verify the existence of opposite sentiment roles for each emoji considered in the study -represented by means of visible acts of meaning. 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. ## related work walther and d'addario (2001) studied the sentiment impacts of emoticons in computer-mediated communication (cmc). for the first time, they proposed to study emoticons and plain verbal messages as a whole. they studied the impacts of positive and negative emoticons on positive and negative verbal messages. in the paper, it is reported that positive emoticons increase the positivity of positive verbal messages, but negative emoticon do not increase the negativity of negative messages. they found that while the emotional valence of text (e.g., "i am happy") tends to be more important than any accompanying emoticons with respect to interpretation, a negative emoticon (e.g., the frowning face: regarding emoji, herring and dainas (2017) identified eight mutually exclusive pragmatic functions of graphicons (i.e., emoticons, emoji, stickers, gifs, images, and videos) use (reaction, action, tone modification, mention, riff, narrative sequence, ambiguous, and other) in comments on facebook groups, taking the discourse context into account. the results of their analysis showed that emoji were the most used graphicon and also expressed the widest range of pragmatic functions, especially reaction and tone modification. on the other hand, @xcite identified seven intentions underlying emoji use (expressing sentiment, strengthening messages, adjusting tone, expressing humour, expressing irony, expressing intimacy, and describing content) and had respondents rate how likely they were to use 20 individual emoji to express each intention. according to @xcite , an emoji when used in an arabic language context, and perhaps in other langauges as well, can be a true sentiment indicator, a multi-sentiment indicator, an ambiguous sentiment indicator, or a no-sentiment indicator. ## study design the objective of this work is to construct an approach to the analysis of the sentiment effects of emoji in textual content. we intended to analyse these effects through the differentiation in the sentiment labels of texts with and without emoji. besides the labels, we also intended to investigate the nuance impact of emoji on the sentiment, like negativity mitigation or positivity emphasis, by analysing the sentiment intensity of the texts (i.e., their sentiment scores). generally, the change in a text's sentiment with and without emoji inclusion implies the impact of that emoji on that text. we refer to this as an emoji sentiment state. for example, if the text with and without emoji is annotated as positive, then the sentiment state will be keep-positive. however, if the sentiment of the text changes after adding emoji from positive to negative, then the sentiment state will be reverse-tonegative. we assumed that there are seven possible emoji sentiment states that might occurs in such a comparison. comparing these states with the sentiment of the emoji itself will lead us to know the emoji sentiment role in a text. presumably, there are eleven possible sentiment roles that an emoji can have within a text. figure 1 summaries our model used in this study. a detailed description of how we obtained these states and roles and how the result analysis has been done follows. ## results analysis and discussion in the analysis, we found eleven of the defined sentiment roles within the dataset, as shown in in the positive norm, the "face with tears of joy" emoji is found, in some cases by itself (i.e., the emoji @xmath0, playing roles such as: positivity emphasizer and positivity trigger, as shown in figure 3 . besides the mentioned positive roles, this emoji also co-occurs with other positive emoji (e.g., , , , , , and ) to play roles such as: positivity indicator and positive reverser. examples 6, 7, 8, 9 and 10 in figure 4 illustrate this emoji acting as a positivity emphasizer, positivity indicator, positivity mitigator, positive reverser and positivity trigger, respectively. in these examples, we could conclude some positive meanings from the stated texts, like encouragement, complement, humour, and positive response; based on the positive sentiment roles of the co-existing emoji. the "face with tears of joy" emoji has been found 421 times by itself playing a negativity mitigator role within negative text (which has a sense of positivity)(see figure 3 ) but it has not been found, when standing alone, playing any other roles in the negative sentiment norm. for behaving negatively, this emoji was always found co-occuring with other negative emoji (like , , , , , and ). examples 1, 2, 3 and 4 in figure 4 illustrate this emoji taking the role of a negativity emphasizer, negativity indicator, negativity mitigator and negativity trigger, respectively. in these examples, we could infer some negative meanings from the relevant texts, like sarcasm, bullying, complaining and regret, based on the negative sentiment roles of the co-occuring emoji. moreover, we found one case where this emoji played the neutral-effect sentiment role. this is shown in example 5 in figure 4 . the combination of the mixed sentiments of the text and the emoji used within it, makes the message become neutral. thus, none of the contained emoji has a distinguished sentiment effect on this text. note that, in our model, the sentiment intensity of the co-existing emoji is an important factor in determining an emoji's sentiment role in a text. furthermore, emoji sentiment intensity is affected by the emoji load in a text. for instance, in figure 4 , example 1 has two emoji ( and ), while example 3 has the same emoji with different load (i.e., three emoji) ( , and ). the intensity of the emoji is -0.6879562 (the minus sign represent the negativity not the score value), which is higher than the intensity of the (0.2724255). therefore, the emoji with higher intensity dominates the one with lower intensity, making the emoji become a negativity emphasizer through this pattern with (-0.0404245) sentiment intensity 2 . on the other hand, because of the duplication of the emoji in example 3 (i.e., a negative text), its positive intensity in this specific text becomes higher than the negative intensity of the emoji in a way that makes the emoji play a negativity mitigator sentiment role via this emoji pattern 3 . ## conclusions and future work this study objectively depicts all the possible emoji sentiment roles that researchers interested in sentiment analysis might encounter when they are dealing with emoji within textual contents. we have investigated the sentiment roles of emoji within textual content, by investigating their general emojisentiment co-occurence behaviours. accordingly, we defined a set of emoji roles in the sentiment analysis of the accompanying texts. then, we analyzed the results to confirm the existence of opposing sentiment roles for each emoji considered in the study. to this end, we concluded that an emoji can be an emphasizer, an indicator, a mitigator, a reverser or a trigger of negative or positive sentiments; in addition, each emoji might have a sense of no-sentiment-effect that reflects the neutral sentiment norm. nevertheless, investigating, deeply, the impact of the emoji sentiment roles stated here, on the semantics of texts should be considered in the future. in addition, an extended and detailed analysis is needed for the other common emoji rather than just for the "face with tears of joy" emoji. besides, a study on how the presence of emoji might affect the performance of fine-tuned sentiment classification models for arabic can be one of the future considerations. finally, we recommend reproducing this work with different languages in order to understand the similarities and differences of the emoji sentiment roles across different cultures and languages.