A Quantitative Analysis of Comparison of Emoji Sentiment: T aiwan M andarin Users and E nglish Users (2022)

Abstract: Emojis have become essential components in our digital communication. Emojis, especially smiley face emojis and heart emojis, are considered the ones conveying more emotions. In this paper, two functions of emoji usages are discussed across two languages, Taiwanese Mandarin and English. The first function discussed here is sentiment enhancement and the other is sentiment modification. Multilingual language model is adopted for seeing the probability distribution of the text sentiment, and relative entropy is used to quantify the degree of changes. The results support the previous research that emojis are more frequently-used in positive contexts, smileys tend to be used for expressing emotions and prove the language-independent nature of emojis.

## introduction with the considerable growth of social media, emojis have become increasingly popular and widely used across the world. during the last few years, emojis change the way we communicate online. they allow us to interact with each other more clearly when we struggle to express our emotions through pure texts. it is an explicit acknowledgment that emojis are now part of how we express our emotions, intents and feelings. aside from emotion expression, emojis are believed to enhance and modify the sentiment of a text. a sentence may convey an emotion, but its emotion would be strengthened after the addition of emojis, which is called sentiment enhancement. on the other hand, if the emotion of the sentence is weakened or altered to the opposite emotion, that is called sentiment modification. moreover, research shows that emojis used to convey emotions are mostly smileys and hearts, which belong to the smiley & emotion group in the unicode emoji categories. however, though emojis' non-verbal nature suggests that they are universal across cultures, their usages may change from language to language and culture to culture. over the past few years, great concern has arisen in the research of cross-cultural or cross-language emoji usage. some of them discuss which kinds of emoji patterns are the same or different across cultures. some studies build the emoji sentiment lexicon with the data from different languages. but little was done on the degree of how each emoji can enhance or modify the text across languages. this paper aims to compare the degree of sentiment enhancement and sentiment modification of emojis which are used by taiwanese mandarin users and english users with the help of a multilingual language model. recently, large pre-trained neural models such as bert have achieved great success in nlp, motivating more and more research to investigate what aspects of language they are able to learn from unlabeled data. ## background and related work many people believe emojis are like the older emoticons, which provide a visual representation using punctuation marks. emojis and emoticons function as the non-verbal cues (paralanguages) in face-to-face communication, which are believed to convey emotions more effectively and efficiently than the words themselves are saying. in fact, the reason punctuation marks came into existence was to complement emotional engagement in written texts. @xcite propose that emojis not only add an emotion to a sentence, but also enhance and modify the emotion of a sentence. they also state that emojis are ambiguous and unreliable without context and emojis are often placed at the end of sentences to express emotions. further works are done to support that emojis and emoticons result in higher sentiment and have higher contribution in overall sentiment score. @xcite uses knn-like strategy to show that punctuations, words and pattern features (including emoticon tags) can improve the quality of sentiment classification tasks. @xcite suggest that specific features like emoticons and hashtags also add marginal value to the sentiment classifier. @xcite puts forward that sentiment classifiers are more accurate when they train on emoticons. @xcite collects positive and negative data to analyze the influence of emojis in sentiment analysis, they find that emojis not only increase sentiment score in both polarities, but more frequently used to show positive opinions. tian's study on facebook data across four different countries (2017) proves that emojis and texts can update the meaning of each other, suggesting there is a correlation between emojis and linguistic contexts, the author also states that sarcasm, irony and politeness can be interpreted by analyzing emojis. however, previous works mainly take emojis as features to better the performance on sentiment analysis. this approach would not take the impact of emojis on the texts into account, since an emoji has different influences on different contexts. we can know a smiley has a positive impact on texts, but it might be difficult to obtain how much degree of the impact of a smiley on two unrelated texts. along with the development of the attention network, lou et al. ( 2020 ) first use attention mechanisms to train emoji and text embeddings simultaneously on a bi-lstm model. conneau et al. (2020) present a transformer-based multilingual pre-trained on texts in 100 languages. the most widely used genre among emoji is facial expressions, gao (2020) states that they are keys to convey emotions. when people look at a smiley face online, the same parts of the brain are activated like they look at a real human face. however, facial expressions are not universal signals. the interpretation of emotions and attitude is strongly influenced by different cultural backgrounds. @xcite according to the study, overt emotional demonstration is the norm in western cultures, while subtle emotional demonstration is the norm in eastern cultures. researchers also suggests that these differences extend to the use of emojis. @xcite from another perspective, there are researches exploring the meanings and usages across cultures and languages. @xcite adopt various experiments to compare the usage of emojis across four western languages. they observe that the frequently used emojis share similar semantic usages across these four languages, supporting that emojis are language independent. on the other hand, they find that the usages of particular emojis differ due to the cultural influences. ## results and discussion frequency of emoji usages: figure 1 shows 20 most frequently seen in both languages. across two languages, spearman correlation coefficient (scc) is 0.767, indicating that two groups of different language users favor similar types of emoji. "face with tears of joy" emoji has high ranks in two languages. figure 2 and figure 3 show the frequency of emoji categories and their scc values. the values range from 0.32 to 0.74, depending upon categories. not surprisingly, the frequency of emojis in "smiley and emotions" exceeds other categories greatly in both languages. there are relatively low correspondences in "animal & nature", "travel & place", "objects" and "flags". to drill down the details, the data shows that taiwanese mandarin users use more animals while english users use more plants. in the "activity", the highly frequent emojis are related to birthday or celebration, which is quite different from the previous research @xcite . the correlation of emojis in "activity" category in their research across the east and the west users is low. figure 5: the scatter plot of emojis used by taiwanese mandarin users and english users in negative enhancement. (r @xmath0.547) figure 6: the scatter plot of emojis used by taiwanese mandarin users and english users in positive modification. (r @xmath1.751) figure 7: the scatter plot of emojis used by taiwanese mandarin users and english users in negative modification. (r @xmath2.771) sentiment enhancement: figure 4 and figure 5 show the scatter plots of emojis for sentiment enhancement in both languages. in figure 4 , 192 types of emojis are used for positive enhancement, the amount is more than emojis used for negative enhancement (only 74 types). moreover, smileys and hearts highly increase positive feelings in both languages, whereas emojis in other categories also enhance the positive sentiment. therefore, emojis are frequently used in positive feelings and convey positive feelings in general. in figure 5 , most of the emojis for negative enhancement are classified into negative emojis (sad faces and angry faces), which are considered to increase negative feelings. and most of the emoji types for negative enhancement belong to the "smiley and emotion". the percentage of emoji types in "smiley & emotion" category is 22.3% for positive enhancement and 72.3% for negative enhancement. and the total number of smileys in positive enhancement and negative enhancement across two languages are over 99%. sentiment modification: figure 6 and figure 7 show the scatter plots of sentiment modification in both languages, the usages for this purpose across two languages have relatively high correlation, implying that the effects of emojis might surpass the texts in both languages. compared with sentiment enhancement, the emoji types for sentiment modification are relatively low with 42 types for positive modification and 34 types for negative modification. the percentage of emoji in the "smiley & emotion" category is 47.6% for positive modification and 79.4% for negative modification. similar to negative sentiment enhancement, the percentage of smileys faces and hearts emojis are higher than the positive ones. the total number of smileys in positive modification and negative modification across two languages are over 99%. ## conclusion in this research the sentiment degree of emojis used by taiwanese mandarin users and english users is compared. for the similar part, the usages of smileys and hearts support the agreement that emojis can be used universally. while there is no significance in the difference in two languages due to data amount. these are only preliminary results, more extensive analyses of the function of emojis are planned to run further.