Systematic Review ARTICLE
A Systematic Review of Emoji: Current Research and Future Perspectives
- 1The School of Journalism and Communication, Renmin University of China, Beijing, China
- 2Research Center of Journalism and Social Development, Renmin University of China, Beijing, China
A growing body of research explores emoji, which are visual symbols in computer mediated communication (CMC). In the 20 years since the first set of emoji was released, research on it has been on the increase, albeit in a variety of directions. We reviewed the extant body of research on emoji and noted the development, usage, function, and application of emoji. In this review article, we provide a systematic review of the extant body of work on emoji, reviewing how they have developed, how they are used differently, what functions they have and what research has been conducted on them in different domains. Furthermore, we summarize directions for future research on this topic.
With the widespread application of computing and the development of technology, computer mediated communication (CMC) is infiltrating daily life to a greater and greater extent. It has many advantages, including enhancing the continuity of individual communication (Juhasz and Bradford, 2016), improving the quality of relationships (Pettigrew, 2009; Perry and Werner-Wilson, 2011), and strengthening emotional communication (Derks et al., 2008b). However, the lack of non-verbal cues such as facial expressions, intonation, and gestures in CMC can affect the transmission of information (Archer and Akert, 1977). To address this problem, communicators have devised new non-verbal cues, such as capitalization as a substitute for shouting, multiple exclamation points for excitement, and expression symbols for facial expressions (Harris and Paradice, 2007; Riordan and Kreuz, 2010). These expression symbols make up for the lack of non-verbal cues in CMC (Tossell et al., 2012; Negishi, 2014), and are very well-suited for social media communication (Barbieri et al., 2016c). As a result, emoji, which are a set of expression symbols, came into being.
Emoji are used more and more frequently in network communication, and the way they are used is becoming more and more diversified as well. They not only have unique semantic and emotional features, but are also closely related to marketing, law, health care and many other areas. The research on emoji has become a hot topic in the academic field, and more and more scholars from the fields of computing, communication, marketing, behavioral science and so on are studying them. This paper reviews the developmental history and usage of emoji, details the emotional and linguistic features of emoji, summarizes the results of research on emoji in different fields, and puts forward future research directions.
Literature Search and Criteria for Inclusion
We started by selecting databases. With reference to the major platforms for related publications, we chose Web of Science and Google Scholar as literature sources. On August 3rd, 2019, we searched Google Scholar and Web of Science using “emoji” as the key word for related literature in English since 1998. Patents, news, book reviews, editorials, letters, and other literature types were excluded and the two databases were combined. After getting rid of the duplicates, we got 167 papers published in 78 journals and and delivered at 33 conferences. We summarized the list of journals and conferences that published articles on emoji more than once, as shown in Table 1. In addition, due to the extensive use of emoji in social networks, we also referred to some online data sources, such as https://emojipedia.org/, https://www.reddit.com/r/EmojiReview/. In this paper, the above contents were summarized and reviewed.
In recent years, emoji have become a hot topic for research, with the volume of papers increasing gradually from 2015 and peaking at 2017-2019. Research mainly comes from the fields of computer science and communication science. Marketing, behavioral science, linguistics, psychology, medicine, and education are also involved. Research mostly uses empirical analysis, focusing on the diversity of individuals, cultures and platforms in the use of emoji, the attributes and characteristics of emoji, their functions in communication and the application of emoji in various research directions. Table 2 systematically summarizes the main research fields, research topics, main conclusions and research methods for emoji.
Table 2. Main research fields, research topics, main conclusions, research methods, and number of publications for emoji.
In addition, a lot of researches on emoji are cross-field. For example, as emoji are platform-or system-dependent, they are often used in online communication. Due to its visual characteristics or platform differences, there would be emotional or semantic ambiguity in communication. Many researchers from computer science try to solve this problem using a computer method and a series of algorithms or models for semantic disambiguation and sentiment analysis have been developed. Besides, the use of emoji is associated with psychological differences. Some researchers in the field of psychology have also focused on emoji usage to search for the relationship between user's behavior and personality traits. What's more, emoji is used in marketing activities to enhance interaction and promote consumers' willingness to purchase. In order to make better use of this symbol, researchers from the field of marketing draw on relevant theories in the field of linguistics, especially in rhetoric, to enhance the appeal of emoji in marketing activities.
The Development of Emoji
Emoji originated from smiley, which first evolved into emoticons, followed by emoji and stickers in recent years. Smiley first appeared in the 1960s and is regarded as the first expression symbols. Smiley is a yellow face with two dots for eyes and a wide grin which is printed on buttons, brooches, and t-shirts. By the early 1980s, this symbol had become widespread, emerging as a permanent feature of western popular culture (Stark and Crawford, 2015).
Emoticons were introduced in 1872 and use ordinary punctuation marks from a standard computer keyboard to build up a representation of a face with a particular expression (Zhou et al., 2017). They are a paralinguistic element (Lee and Wagner, 2002; Jibril and Abdullah, 2013) often used at the end of a sentence (Sakai, 2013). Prior to the existence of emoji, users of Instant Messaging (IM) would often use emoticons. Like non-verbal clues in face-to-face communication, emoticons can help clarify intentions in ambiguous contexts (Thompson et al., 2016), express emotions (Walther and D'Addario, 2001; Aldunate and Gonzálezibáñez, 2016; Wall et al., 2016; Esposito et al., 2017) and improve the efficiency of communication (Dunlap et al., 2016). Besides, emoticons possess nonverbal communication functions. They can help those receiving them correctly understand the sender's emotion, attitude, and level of attention (Lo, 2008), bring enjoyment (Chen and Siu, 2017), promote interaction (Aldunate and Gonzálezibáñez, 2016) and community identity (Cho, 2016). In practice, gender, and cultural differences lead to different preferences for emoticon usage (Wolf, 2000; Jack et al., 2009). It has also been suggested that emoticons could be applied to real life, for example in fields such as emotional monitoring (Carvalho et al., 2009; Barbieri et al., 2014), psychological testing (Tan et al., 2018) and designing signs (Sodikin, 2018).
The first set of emoji was released in 1999 and was created by their Japanese originator Shigetaka Kurita (). “Emoji” is a transliteration of the Japanese word (e=picture) 文 (mo=write) 字 (ji=character). They are graphic symbols with predefined names and code (Unicode), which can represent not only facial expressions, abstract concepts and emotions/feelings, but also animals, plants, activities, gestures/body parts, and objects (Rodrigues et al., 2017). Possessing similar neural responses to face-to-face communication (Gantiva et al., 2019), using emoji can add extra emotional or contextual meaning to communication, enhance the attractiveness of the message to receivers (Cramer et al., 2016), help users in tone adjustment and conversation management and play a role in managing and maintaining interpersonal relationships (Kelly and Watts, 2015; Chairunnisa and Benedictus, 2017; Albawardi, 2018). On a social level, emoji, as a visual language, make it easier for non-English speaking nations to use English-dominated social media such as Twitter, Instagram and Facebook (Boothe and Wickstrom, 2017). Emoji are widely used in instant messaging, e-mail, social networking and many other forms of CMC (Dresner and Herring, 2010). As indicates, emoji fill the need for non-verbal cues in CMC to express the intentions and emotions behind information (Alshenqeeti, 2016).
In recent years, in order to realize the interpretability of information transmission and better express its meaning, stickers came into being (Zhou et al., 2017). Stickers can help users strategically and dynamically choose the best way to express their emotions, opinions, and intentions and to achieve communicative fluidity (Lim, 2015). At the same time, stickers can be used for strategic motives such as self-presentation, impression management, establishing social existence and maintaining social status (Lee et al., 2016). Besides, responding to a partner with a combination of text and stickers can establish a high level of intimacy (Wang, 2016).
Smiley, emoticons, emoji, and stickers differ in form and content, and have been favored by users in different periods. Smiley, often used in advertisements and product packaging, can encourage positive moods and improve morale (Stark and Crawford, 2015). Unlike emoji, emoticons, and stickers which possess a whole set of characters, smiley is a single symbol rarely used in communication. Emoticons present facial expressions by various combinations of punctuation marks and can be used in CMC. Studies have shown that smiley and emoticons have no difference in information interpretation, but that smiley has a greater impact on individual mood than smiling emoticons (Ganster et al., 2012). Emoji have come to be regarded as an advanced version of emoticons (Aull, 2019), and are superior to emoticons in terms of content richness, input speed and expressiveness (Barbieri et al., 2016b; Rodrigues et al., 2017). Because both act as auxiliary means of communication, emoji and emoticons are completing for similar functions. But the emergence of emoji has been proven to impact the status of emoticons to a certain extent. Compared to emoticons, users use emoji more frequently, with a more positive attitude and a deeper level of identification (Prada et al., 2018). Stickers appeared recently. They are bigger, with static and animated forms, can be added or deleted (emoji rely on Unicode which can't be edited). But stickers can only be sent separately without insertion in text messages (Zhou et al., 2017). Table 3 summarizes the differences between smiley, emoticons, emoji, and stickers.
As emoji currently make up the most widely used and standardized symbolic language with the largest number of existing studies, this paper mainly reviews and discusses related research on emoji.
The Use of Emoji
As non-verbal cues in CMC, emoji are widely used in internet communication. As of March 2019, there were 3,019 emoji in Unicode, with nearly half of all text messages on Instagram containing emoji (Dimson, 2015), and 5 billion of them being used daily on Facebook. In 2015, emoji was named the word of the year by the Oxford English Dictionary, indicating emoji's influence in online communication.
Simplicity, convenience and conduciveness to emotional expression are the main motivations attracting users to use emoji. Specifically, emoji can help users to express themselves, relax their mood (Kaye et al., 2016) and build their own identity (Ge and ACM, 2019). As contextualization cues (Al Rashdi, 2018), emoji are used in communication to promote interaction (Gibson et al., 2018), including establishing emotional tone, reducing discourse ambiguity, enhancing context appropriateness (Kaye et al., 2016) and intensifying or weakening speech acts (Sampietro, 2019). In addition, emoji are also used to greet (Aull, 2019), and to maintain and enhance social relations while strengthening communication within a platform (Monica Riordan, 2017b). However, some researchers point out that emoji may also be maliciously used for deception (Njenga, 2018).
Diversity of Emoji Use
In the process of using emoji, the differences between individual characteristics, platforms, cultural backgrounds, and contexts may lead to different understandings. Emoji are also used for some specific topics, such as in sexually suggestive contexts (Thomson et al., 2018). This paper systematically summarizes the differences of emoji in terms of individual diversity, cultural diversity, platform diversity, and their inefficiency in use.
The use of Emoji is influenced by demographic characteristics and individual psychological characteristics.
First of all, there are significant gender differences. Although males and females understand the function of emoji similarly (Herring and Dainas, 2018), females use emoji more frequently and positively (Prada et al., 2018) while males use more types of emoji (Tossell et al., 2012). However, this trend varies according to communication situation. In public communication, women are more likely to use emoji while in private communication the opposite is true (Chen Z. et al., 2018). In terms of the cognition of emoji, females perceive emoji as more familiar, clear and meaningful (Rodrigues et al., 2017). Male users prefer to use the same emoji to enhance emotional expression (Chen Y. et al., 2018). When men and women use the same emoji, the recipients feel different emotions. Women who send messages containing affectionate emoji are considered more appropriate and attractive than men, and when men send messages containing less affectionate but friendly emoji messages, they are considered more appropriate and more attractive than women (Butterworth et al., 2019).
The use of emoji is also affected by individual psychological differences. This has been shown in research which demonstrates a positive correlation between the frequency of emoji use among Facebook users and their extraversion and self-monitoring traits (Hall and Pennington, 2013), and a negative correlation between positive emoji use and users' emotional distress (Settanni and Marengo, 2015). An emoji-based personality test indicated that the similarity score between emoji and oneself was correlated with emotional stability, extroversion and agreeableness out of the Big-Five personality traits, but not correlated with conscientiousness and openness (Li et al., 2018). Specifically, negative emojis were negatively correlated with emotional stability, while positive emoji were positively correlated with extraversion. In addition, emojis associated with blushing (e.g., ) were positively correlated with agreeableness.
As people become more and more enthusiastic about using emoji, some emoji forums emerged, such as https://www.reddit.com/r/EmojiReview/. In the forum, people communicate with each other to explore the various uses and meanings of emoji. With the increase of the need to express individual diversity, people are no longer satisfied with using the existing emoji in the system, but began to create their own expressions and add more personal characteristics to emoji. For example: . These are new symbols created by people after recombining existing emoji.
Emoji use is structured by a combination of linguistic and social contexts, as well as cultural conventions (Derks et al., 2008a; Park et al., 2013), and is influenced by many factors, such as cultural background, living environment, language environment and user group.
Cultural differences have a significant impact on the use of emoji. Some specific uses of emoji are closely related to cultural background (Park et al., 2014). For example, Finnish, Indian and Pakistani users will use specific emoji according to their own culture (Sadiq et al., 2019). In terms of usage behavior, following Hofstede's cultural dimension model, people in countries with high power distance and indulgence use more emoji representing negative emotions, while people in countries with high uncertainty avoidance, individualism and long-term orientation often use emoji representing positive emotions (Xuan et al., 2016). Specifically, Chinese users are more likely than Spanish users to use non-verbal cues such as emoji and emoticons to express negative emotions (Cheng, 2017). Research has also found that people from Hong Kong and the US use emoji differently on user-generated restaurant reviews websites, which may reflect underlying cultural differences (Chik and Vasquez, 2017). Because of the cultural differences in emoji use, an EmojiGrid was developed for cross-cultural research on food-related emotions, which reliably reflects established cultural characteristics (Kaneko et al., 2019). This difference is not only evident between countries, but also within the same country (Barbieri et al., 2016a).
Emoji use is partly influenced by a range of national developmental indicators (including life expectancy, tax rates, trade and GDP per capita). One line of research using the K-MEANS clustering algorithm found that the most distinctive feature of emoji use in the “first world” (defined here as North America, Western Europe, the Russian Federation, and Australia) was a lack of emotions, while in the “second world” cluster (covering most of South America, Eastern Europe, India, China, Eastern Europe, Morocco, Algeria, and Tunisia) emoji are used in a more specific, emotionally clear way. The “third world” cluster (Angola, Nigeria, Sudan, Jordan, Saudi Arabia, Yemen, Pakistan, Nepal, and the Philippines) uses a balance of positive and negative emoji, and the “fourth world” cluster (made up of certain African countries) uses mostly negative emoji (Ljubešić and Fišer, 2016).
Specific language environments also affect the use of emoji. Emoji show a high degree of context sensitivity in cross-language communication, meaning that they are exceedingly dependent on their linguistic and textual environment (Vandergriff, 2013). For example, research suggests strong similarities in emoji use between Britain and America due to the fact that they both speak English, but there was less similarity when the comparison was made with other languages, such as Italian and Spanish (Barbieri et al., 2016b).
There are also differences in the use of emoji among specific cultural groups. One example of this is a particular style of mobile communication creatively balancing use of emoji, stickers, and text developed by some adults in rural and small towns in Southern China (Zhou et al., 2017). Research has also shown that Japanese teenagers find innovative new ways to use emoji so as to manage their relationships and express themselves aesthetically in a subculturally specific way (Sugiyama, 2015). The use of Emoji is also related to interpersonal relationships (Gaspar et al., 2015, 2016). The more polite and distant the conversation between people, the more abstract, geometric and static the emoji will become. On the contrary, more specific and vivid emoji is used in groups where participants are more sympathetic to a particular topic, more companionate and more intimate (Lin and Chen, 2018).
Platform diversity is one of the important factors affecting emoji use. The presentation style of emoji on different operating system platforms and the architectural specifications of different network platforms will affect users' preferences for emoji.
Although emoji use Unicode, the presentation style of emoji in IOS, Android, Microsoft and other systems is different due to the influence of different developers (as shown in Table 4). Studies have found that emoji on the IOS platform are more aesthetically attractive, familiar, clear and meaningful than those on the Android platform (Rodrigues et al., 2017). This difference in platform display will lead to misunderstanding and divergence in terms of emoji's emotional and semantic interpretation during cross-platform use (Tigwell and Flatla, 2016). In addition, researchers have studied different network platforms such as Twitter, Facebook and Instagram, and found that users of different platforms have their unique preferences when using emoji. The most popular emoji in one platform may not be popular on other platforms. For example, users tend to use emoji more frequently and positively on Facebook than on Twitter (Tauch and Kanjo, 2016). At the same time, some researchers focus on more marginal community platforms like Gab. In the face of the same event, Gab users tend to publish positive emoji to express irony in text with negative connotations, while Twitter users tend to use emoji to express suspicion (Mahajan and Shaikh, 2019). However, some researchers believe that the use of emoji is generally consistent on all platforms, except for e-mail, which is not suitable for using emoji (Kaye et al., 2016).
Emoji can help users to convey feelings and understand the meaning of a text, but the use of emoji also brings ambiguities in the interpretation of communication, resulting in inefficiency. Although emoji have visual similarity, their interpretation is influenced by cultural background, technical differences and their own visual characteristics (Bich-Carriere, 2019). The specific meanings that users want to express by emoji may be different from their official definitions, resulting in different interpretations of the same emoji (Miller et al., 2016) (Table 5). For example, some people interpret this emoji () as “prayer” and others interpret it as “clapping hands”. In this case, it is difficult for the two sides to understand each other, which reduces the efficiency of communication. Berengueres and Castro (2017) found that there are differences in understanding negative emoji. For the same negative emoji, the sender's emotional feelings can be 26% different from the receiver's. Research done by Riordan (2017a) shows that the degree of misunderstanding of facial emoji is higher than that of non-facial emoji, but that both are related to the degree of information ambiguity. When used across platforms, the differences in how people interpret emoji emotionally and semantically will increase because of platform display differences (Miller et al., 2016). The difference in how emoji are understood results in inefficiency in communication, leads to the interruption of discourse and destroys interpersonal relationships (Tigwell and Flatla, 2016).
Functions of Emoji
As an important visual symbol in computer-mediated-communication, emoji can express various content, including people, animals, food, activities. Emoji can be used both as an independent language and a non-verbal cue to convey meanings, which is the semantic function of emoji. In addition, emoji also have emotional functions. We have summarized them in Table 6.
The Emotional Functions of Emoji
Because they are non-verbal cues with rich emotional meanings, emoji are an important medium for interaction and emotional communication on the Internet.
Emoji can express or enhance emotions (Gülşen, 2016). Jaeger and Ares (2017) analyzed 33 facial emojis and found that most emoji can express one or more emotions. The rich emotional meaning of emoji makes them a key area for researchers who analyze their emotions and develop emoji emotional lexicons. By artificial annotating, Petra et al. (2015) divided emojis into positive, negative and neutral according to their emotional distribution, and found that most emojis were positive, but there were also some emojis which can express irony or satire (Vanin et al., 2013). Due to the subjectivity of human annotating, some researchers have proposed the automatic construction of emoji lexicons. Fernandez-Gavilanes et al. (2018) automatically constructed an emoji lexicon based on the official definitions in emojipedia.
Because of their rich emotional meanings, emoji are often used to express emotions in online communication. In general, users tend to use emoji in positive messages and to use them less in sad or angry messages (Cheng, 2017). Different emoji affect people's attention and responses in divergent ways (Hjartstrom et al., 2019). Although both facial and non-facial emoji can express emotions (Riordan, 2017a), facial emoji outperform non-facial emoji (Jaeger et al., 2019). Using non-facial emoji can bring about positive emotions, especially joy, but it can't change the valence of the message (Riordan, 2017b). Different combinations of emoji also have subtle differences in emotional expression, for example, López and Cap (2017) found that when combining frog emoji or hot beverage emoji with other emojis, there will be subtle but observable emotional changes.
The Semantic Function of Emoji
In addition to expressing emotions, emoji are also used to convey semantic meanings in communication (Na'aman et al., 2017). They can play the role of non-verbal cues to help understand the overall meaning of messages in CMC (Walther and D'Addario, 2001; Jibril and Abdullah, 2013). There has been a lot of discussion about whether emoji could become an independent language. In addition, due to the diversity and similarity of emoji semantics, many researchers from the field of computing pay attention to the word sense disambiguation task of emoji.
Some research suggests that emoji form an independent language. They have a semantic function and visual rhetoric function, can convey meanings as an independent expressing modality (Jibril and Abdullah, 2013), and, through the combination of different emoji, can express subtler semantics (López and Cap, 2017). Compared with plain text, emoji are richer in semantic meaning (Ai et al., 2017), and have semantic similarity in different languages (Barbieri et al., 2016b). At the application level, Khandekar et al. (2019) developed the social media app called Opico to explore the possibility of “emoji-first” communication, which proved that emoji can be used independently in communication without the need for text. However, some researchers suggest that emoji can't be used as an independent language. Lee et al. (2019) found that emoji are similar to the radicals of Chinese characters. Alshenqeeti (2016) argues that emoji is essentially a form of visual paralanguage. Furthermore, emoji tend to be text-related and rarely used independently. Emoji need to be integrated with the text in order to form a complete meaning (Zhou et al., 2017), helping to enhance the clarity and credibility of the text (Daniel and Camp, 2018). In practice, users tend to use emoji as a supplement to text (Ai et al., 2017; Donato and Paggio, 2017), which also indicates that emoji is a paralanguage.
The meaning of emoji varies according to specific context (Gawne and McCulloch, 2019). Their diversity of semantics and flexibility of interpretation may lead to ambiguity when using them (Jaeger et al., 2019). Therefore, a lot of research focuses on the word sense disambiguation task of emoji. Wijeratne et al. (2017) has developed an Emojinet, which combines emoji and text to eliminate ambiguity. Barbieri et al. (2016c) improved the Skip-Gram model, analyzed the semantics of emoji on twitter, and classified them based on semantic similarity.
Research Fields Regarding Emoji
Emoji have both emotional and semantic functions and are popular in computer-mediated-communication. Researchers from different fields have studied emoji from different perspectives, including computer science, communication, marketing, behavioral science, linguistics, psychology, medicine, and education.
Research in the field has focused on using emoji for emotional analysis of UGC data, the conversion of emoji to other expression modality, and using emoji for optimizing computer systems.
With the significant growth of UGC data on the Internet, sentiment analysis which aims at changing this data into valuable asset for decision making, has become increasingly important (Al-Azani et al., 2018). As emoji are widely used in expressing emotions, they have become an effective means of sentiment analysis (Hogenboom et al., 2013; Cappallo et al., 2015). A number of studies have confirmed the effective performance of emoji in sentiment analysis (Sari et al., 2014; Cahyaningtyas et al., 2017; Felbo et al., 2017; LeCompte and Chen, 2017). Besides, emoji-based sentiment analysis is language-independent and exhibits cross-language validity (Guthier et al., 2017), for example, Al-Azani et al. (2018) found that emoji can also be used in analyzing the sentiment of Arabic tweets. However, other studies have shown that using emoji in sentiment analysis leads to higher emotional scores, and that this effect is more pronounced in positive comments (Ayvaz and Shiha, 2017).
Many studies have provided algorithms and models for emoji-based sentiment analysis, which mainly uses two kinds of techniques, sentiment lexicon, and machine learning. The sentiment lexicon approach focuses on building an emoji emotional lexicon to support text sentiment analysis. By human annotating, Petra et al. (2015) has classified 751 commonly used emoji and built an emoji lexicon based on the positivity of emoji. But because there are so many emoji, some researchers have come up with ways to build emoji dictionaries automatically. Jiang et al. (2015) proposed an emoticon space model to automatically match emotional tags for emoji. Kimura and Katsurai (2017) assigned multi-dimensional emotional vectors to emoji by calculating the co-occurrence frequency of emoji and emotional words in WordNet-Affect. Aoki and Uchida (2011) have also automatically generated emoji vectors based on the relationship between emotional words and emoji. By using the Word2Vec clustering method, Mayank et al. (2016) divided emoji into clusters which represent different human emotions. The machine learning method refers to train sentiment classifiers based on a corpus in order to analyze the sentiments of text (Wang et al., 2012). Machine learning can be divided into supervised learning and unsupervised learning. They are different in that the former needs a human annotated corpus while the latter doesn't. The effectiveness of using emoji as a way of training classifiers has been proven (Hallsmar and Palm, 2016) and furthermore it has been shown that emoji outperform emoticons (Redmond et al., 2017). An example of supervised learning is the emoticon smoothed language model (ESLAM) proposed by Liu et al. (2012), which classifies twitter based on a model trained by a human annotated corpus. A lot of research has focused on unsupervised learning (Li et al., 2018), and constructed sentiment analysis models trained automatically using emoji data sets. Chen Y. et al. (2018) trained sentiment classifiers by via bi-sense emoji embedding and attention-based long short-term memory network (LSTM) in order to analyze the sentiment of messages on Twitter. Wang et al. (2016) designed a hybrid sentimental entity recognition model (HSERM), which classifies emoji into four different emotional categories, and then categorizes the emotional data based on the model. Some research has focused on the ironic features of emoji and developed an irony detection model for emoji in order to improve the accuracy of sentiment analysis of tweets (Reyes et al., 2013; Prasad et al., 2017; Singh et al., 2019).
The visual features and Unicode basis of emoji make them an independent expressive modality that is different from text and pictures (Cappallo et al., 2019). A lot of research focuses on conversion between emoji and other modalities such as text, picture and video.
For example, Emoji2Video offers a way to search for videos using emoji (Cappallo et al., 2015). Later research has focused on the shift from other modalities to emoji. Because of the correlation between emotional categories in text and users' emoji selections, Hayati and Muis (2019) and Zanzotto and Santilli (2018) proposed two different ways to predict emoji based on text. Kim et al. (2019) developed Reeboc, which can analyze chat content, extract different emotions or topics, and then, based on this, recommend emoji to users. The practice of text-based emoji prediction has also been validated in other languages, such as Hebrew (Liebeskind et al., 2019).
Emoji have played a role in improving the performance of computer hardware and software. For example, emoji can be used to achieve diverse in-car interaction design. In order to optimize the functions of the central rear-view mirror, researchers suggest that passengers emotions can be fed back to the driver through emoji and other elements, which can enhance mutual understanding between driver and back-seat passenger (Chao et al., 2019).
Furthermore, emoji can also be applied in the area of password security. Kraus et al. (2017) came up with the EmojiAuth project, exploring how the use of emoji affects the availability of mobile authentication and user experience by adding emoji into passwords. Compared with the Standard PIN (Personal Identification Number) input, a password containing emoji is easier to remember and, thus, emoji-based authentication is a practical alternative to traditional PIN authentication.
In the field of communication, research on emoji mainly focuses on two aspects: one is emoji's emotional and linguistic functions in CMC, the other is how different factors, such as individual characteristics, cultural background and system platform, influence users' preferences for emoji use.
Emoji make up for the lack of non-verbal cues in CMC, and play an auxiliary role in conveying emotion (Gülşen, 2016), expressing semantics (Walther and D'Addario, 2001), and promoting interpersonal communication (Gibson et al., 2018). For example, Jaeger and Ares (2017) analyzed the emotional attributes expressed by 33 facial emojis, and found that most emoji contained one or more emotional meanings. Based on their emotional distribution, Petra et al. (2015) classified emojis into positive, neutral and negative and found that most emojis express positive emotions. Similar studies have found that users tend to use more emoji more in positive messages than negative messages. Both facial and non-facial emoji exhibit a great deal of ability when it comes to expressing emotions (Herring and Dainas, 2018; Jaeger et al., 2019). At the same time, different combinations of emojis can enrich the meanings of emotional expression. López and Cap (2017) studied how emotions change when different combinations of emojis were used. More research in this area is referred to in section The Emotional Functions of Emoji of this paper.
Individual use of emoji is influenced by many factors. The existing research can be divided into three categories: individual characteristics, cultural background and system platform. First, the use of emoji is strongly influenced by demographic characteristics such as gender and age of users. Women use them more frequently and men use them more abundantly (Tossell et al., 2012; Prada et al., 2018). Women use emoji more in public communication, but less in private communication (Li et al., 2018). In terms of social cognition, emoji with stronger emotional meanings are considered more appropriate and lovely for women than for men, while emoji with weaker emotional meanings but friendlier meanings are considered more appropriate for men (Derks et al., 2008a). Secondly, the use of emoji is closely related to the user's cultural background. Users in different countries will use emoji with specific national or ethnic meanings (Gaspar et al., 2016). Finnish users introduce “sauna,” Hindus use “Happy Diwali” and Pakistanis use Namaz symbols for emoji design. Users in different countries tend to use emoji differently. Chinese people use emoji and emoticons more often than Spaniards (Lin and Chen, 2018). Emoji show a high degree of contextual sensitivity and different language types influence the use of emoji. For example, the use of emoji displays a strong correlation among English-speaking countries, while displaying lower correlation among other languages (such as Italian and Spanish) (Barbieri et al., 2016b). Finally, different system platforms also lead to differences in emoji usage. Although emoji has a Unicode in the operating system platform, users show emoji differently in IOS, Android and Microsoft operating systems due to the limitation of these software's developmental compatibility (Cramer et al., 2016). Different social networking platforms such as Twitter, Facebook, Gab and Instagram also have their own particular patterns of emoji usage. For example, users tend to use emoji more frequently and positively on Twitter than on Facebook (Hall and Pennington, 2013). Gab users tend to use positive emoji to express negative emotions, thus showing irony (Settanni and Marengo, 2015). More research is referred to in section Diversity of Emoji Use of this paper.
Due to their visual and emotional attributes, emoji can be used in marketing activities. Emoji play an important role in attracting attention, stimulating social interactions and enhancing the experience of consumers, along with their willingness to purchase (Das et al., 2019). So it is hardly surprising that emoji are frequently used in consumer interactions (Lee et al., 2014; Negishi, 2014). Furthermore emoji are also used to depict consumer emotions (Li et al., 2014). Their dominance in emotional expression makes them an effective tool to measure user's emotions.
Textual paralanguages like emoticons and emoji, can influence the cognition and behavior of consumers in marketing activities (Luangrath et al., 2017; Manganari and Dimara, 2017; Urumutta Hewage et al., 2018), for example, the presence of emoji on food packaging can influence children's dietary choices (Siegel et al., 2015; Luangrath et al., 2017). It has been found that using emoji can enhance the explanatory power, attractiveness, creativity and innovation of marketing activity. With the introduction of emoji in online marketing, more young people are attracted (Yakin and Eru, 2015). Ge and Gretzel (2018) indicate that social media influencers (people who take on the dual roles of marketer and active user of social media) can initiate online interaction by presenting emoji individually or in combination, which can attract consumers to participate in interactions.
Emoji can also be a way of reflecting consumers' emotions, describing user's profiles (Moreno-Sandoval et al., 2018), and especially monitoring the emotions users feel toward products, brands, and services (Rathan et al., 2017, 2018; Phand et al., 2018; Moussa, 2019). It has been found that gender, age and frequency of usage do not affect consumers' ability to describe and distinguish stimuli with emoji (Jaeger et al., 2018b), and certain emojis can help consumers better differentiate product samples (Schouteten et al., 2019). In addition, emoji and emoticons are considered simple and intuitive ways to express food-related emotions (Vidal et al., 2016). Marketers use emoji questionnaires as a common tool to measure user's emotions (Jaeger et al., 2017b, 2019), especially children's food preferences and emotional responses (Gallo et al., 2017; Swaney-Stueve et al., 2018; Lima et al., 2019).
However, some researchers point out that although emoji show more discriminability and simplicity than emotional words in emotional measurement, their multiple meanings could pose a barrier to the survey. Therefore, emoji questionnaires can't directly replace the existing text-based forms of sentiment survey directly. They can, however, act as a complement to the current form (Jaeger et al., 2017a, 2018a).
In the field of behavioral science, research on emoji focuses on three aspects: motivation, preference and influencing factors. There has been abundant research focusing on the motivations behind emoji usage. This research has found that emoji are used for managing and maintaining interpersonal relationships (Chairunnisa and Benedictus, 2017; Riordan, 2017b; Albawardi, 2018), expressing oneself (Kaye et al., 2016), constructing personal identity (Ge and ACM, 2019), facilitating communication and enhancing interaction (Gibson et al., 2018) in interpersonal communication. As a contextual cue, emoji can help users establish an emotional tone, reduce the ambiguity of semantic expression and improve appropriateness relative to context (Kaye et al., 2016). There are two aspects of emoji usage preference. One is users' selection of emoji content and the other is the degree to which there is a match between emotions expressed by emoji and real sentiments. For example, users in different countries introduce elements which are representative of their countries into emoji (Sadiq et al., 2019); and users of one social networking platform prefer to use positive emoji in negative texts to express irony (Kaye et al., 2016).
In the field of linguistics, research focuses on the pragmatic functions of emoji and the possibility that they could become an independent language. Emoji have been identified as having semantic properties, and can be used both as an independent language and as a component of a paralanguage providing users with a means of communication and promoting speech acts and interaction (Jibril and Abdullah, 2013; Alshenqeeti, 2016; Na'aman et al., 2017). There are pros and cons regarding whether emoji can become an independent language. Some researchers believe that emoji possess visual rhetoric and text functions and have more subtle semantics and for this reason deem that emoji can independently express meaning (Jibril and Abdullah, 2013; López and Cap, 2017). An application was developed to verify the possibility of emoji-first communication (Khandekar et al., 2019). Other researchers think emoji can't be regarded as an independent language because their meaning largely depends on surrounding text, and only when they are combined with the text can complete semantics be expressed (Zhou et al., 2017).
Studies in this field mainly focus on two aspects. One is the relationship between individual psychological characteristics and emoji usage, and the other is the introduction of emoji into the scale design and the implementation of new psychological measurement tools. Emoji usage was found to be closely related to some psychological traits such as the big five personality traits, self-monitoring, emotional stress, and others (Derks et al., 2008a; Hall and Pennington, 2013; Li et al., 2018). For example, research has shown that frequency of emoji use correlates with emotional stability, extroversion and agreeableness in the big five personality traits, but not with conscientiousness and openness (Li et al., 2018). At the same time, some studies have attempted to introduce emoji into psychometric scales and have achieved good results in actual measurements (Marengo et al., 2017; Phan et al., 2019).
In the field of medicine and public health, studies on emoji mainly focus on correcting personal behavior and improving doctor-patient communication. Emoji can be used to guide people's behavior regarding health, and it has been shown that using emoji can reinforce correct behavior when it comes to hand hygiene monitoring (Gaube et al., 2018). Furthermore, using emoji can improve communication between doctors and patients and also enhance patients' abilities to manage their own health (Balas et al., 2012; Troiano and Nante, 2018). Some researchers suggest developing a set of emoji specifically to be used for patient care, which could help patients better understand and communicate the challenges they face in health management (Skiba, 2016). In addition, emoji can be used for the identification and prediction of mental illness due to their strength in emotional expression. Marengo et al. (2019) has introduced emoji into the process of depression assessment and found that accuracy of identifying depression was significantly improved.
In the field of education, research focuses on the impact of emoji on learning efficiency. It has been found that the use of emoji in classroom activities will help students better understand what they have learned (Brody and Caldwell, 2019), especially in computer-mediated teaching (online learning Dunlap et al., 2016). Emoji can help young children understand abstract concepts such as security, interpersonal management and emotions and also improve their ability to express themselves (Fane, 2017; Fane et al., 2018).
Future Directions for Research
The Relationship Between Emoji and Real Sentiments
Understanding users' real emotions when they use emoji is important for future research. At present, it is difficult to accurately measure participants' true reactions through self-reporting. Categorizing emotions by amassing a corpus using big data is unable to depict users' complex emotions such as are expressed by emoji at a more detailed level, for example emotions such as shame, anger and so on. Therefore, we hold the opinion that in the future, researchers can use some psychological methods in the corpus test to measure the physiological indexes of participants with professional equipment such as nuclear magnetic resonance, electroencephalography and multipurpose polygraphs to depict users' real emotions more accurately. Future research could also benefit from a more qualitative approach, such as interviews and case studies to learn about emoji use in the context of real-world communication. In practice, some researchers suggest that video and screen shots can be used in concrete operations to observe and record users' choices of emoji during communication (Gibson et al., 2018). We believe that observing whether users' actual facial expressions differ from their selected emoji emotionally in communication can help researchers understand users' psychological mechanism in communication.
Factors Influencing User Preferences in Emoji Use
At present, research focuses on the description of users' preference for emoji, but fails to go deeply into the underlying reasons. Emoji such as “heart” () and “tears of joy” () were found to be more popular, but whether their popularity is related to specific cultural traits has not been studied. Users' preferences for emoji are influenced by many factors such as contextual information, interpersonal relationships, familiarity with emoji and personal interpretations other than official definitions, which are all worthwhile factors to explore.
Sticker's Impact on Emoji
The emergence and widespread use of stickers has impacted the status of emoji, and some research has begun to improve the user experience of stickers (Shi et al., 2019). Whether stickers will replace emoji is an interesting topic for researchers. Under the impact of stickers, how to further enhance emoji's performance in emotion and semantic expression and improve user experience is also a direction worth exploring.
The Relationship Between Emoji Usage and Social Development
As part of popular culture, the development and use of emoji reflects specific political and cultural characteristics. Many researchers have interpreted emoji's social influence from different perspectives. For example, some uncivilized use of emoji can harm public consciousness, a point which is not yet appreciated by the public (Zerkina et al., 2017). Other researchers believe that the popularity of emoji reflects multicultural communication and cultural globalization (Skiba, 2016), and that there is some unconscious power behind the use of non-verbal cues like emoji (Elder, 2018), which strengthen the inequality and exploitation of our social system (Stark and Crawford, 2015). For example, Leslie (2019) argues that the quantitative use of emoji in the workplace (such as the use of emoji to give ratings) has turned the employee into something like an on-the-shelf item in a digital economy warehouse, affecting their freedom.
The democratization of emoji selection and Unicode should also be discussed. Emoji of different skin colors have been introduced to address the lack of racial representation (Sweeney and Whaley, 2019). In addition, the Unicode consortium recently approved emoji that specifically refer to menstruation, which is seen as a step toward getting rid of “menstrual shame,” reflecting that women's rights are on the increase. Therefore, future research can explore the deeper meaning of emoji use from different perspectives, especially the links between emoji use and political movements, subcultural groups, and social inequality.
This paper systematically reviews related research on emoji, aiming to provide a global perspective and clues for researchers interested in emoji. This paper summarizes the developmental process, usage features, functional attributes, and fields of research related to emoji. Emoji developed from emoticons, and have both emotional and semantic functions. The use of emoji is influenced by and varies according to factors such as individual circumstances, culture, and platforms. Ambiguity and misunderstanding may occur in different situations and cultural backgrounds. From the perspective of many fields (communication, computing, behavioral science, marketing, and education), this paper comprehensively combs the research topics, methods and tools used in studies related to emoji, systematically summarizes the research status of emoji in various fields, and puts forward some new perspectives for future emoji research such as emotional association, use preference, new modalities and impacts on society.
Data Availability Statement
The datasets generated for this study are available on request to the corresponding author.
QB contributed conception and design of the study. QB, QD, ZM, and MY wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
This research was granted by Journalism and Marxism Research Center, Renmin University of China (RMXW2018C003).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ai, W., Lu, X., Liu, X., Wang, N., Huang, G., and Mei, Q. (2017). “Untangling emoji popularity through semantic embeddings,” in Paper Presented at the Eleventh International AAAI Conference on Web and Social Media (Montreal, QC).
Al-Azani, S., El-Alfy, E.-S. M., and IEEE. (2018). “Combining emojis with Arabic textual features for sentiment classification,” in Paper Presented at the 2018 9th International Conference on Information and Communication Systems (ICICS) (Irbid). doi: 10.1109/IACS.2018.8355456
Aoki, S., and Uchida, O. (2011). “A method for automatically generating the emotional vectors of emoticons using weblog articles” in Paper Presented at the WSEAS International Conference on Applied Computer and Applied Computational Science (ACACOS'11) 10th (Venice).
Barbieri, F., Kruszewski, G., Ronzano, F., and Saggion, H. (2016b). “How cosmopolitan are emojis?: exploring emojis usage and meaning over different languages with distributional semantics” in Paper Presented at the 2016 ACM on Multimedia Conference. doi: 10.1145/2964284.2967278
Barbieri, F., Ronzano, F., and Saggion, H. (2016c). “What does this emoji mean? A vector space skip-gram model for Twitter emojis,” in Paper presented at the International Conference on Language Resources and Evaluation, LERC.
Barbieri, F., Saggion, H., and Ronzano, F. (2014). “Modelling sarcasm in twitter, a novel approach,” in Paper Presented at the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.3115/v1/W14-2609
Berengueres, J., and Castro, D. (2017). “Differences in emoji sentiment perception between readers and writers,” in Paper Presented at the 2017 IEEE International Conference on Big Data (Boston, MA). doi: 10.1109/BigData.2017.8258461
Bich-Carriere, L. (2019). Say it with a smiling face with smiling eyes: judicial use and legal challenges with emoji interpretation in Canada. Int. J. Semiotics Law 32, 283–319. doi: 10.1007/s11196-018-9594-5
Boothe, D., and Wickstrom, C. (2017). “Esol learners must confront diverging language pathways between social media and english for specific purposes,” in Paper Presented at the 10th International Conference of Education, Research and Innovation (Beijing). doi: 10.21125/iceri.2017.0714
Brody, N., and Caldwell, L. (2019). Cues filtered in, cues filtered out, cues cute, and cues grotesque: teaching mediated communication with emoji pictionary. Commun. Teach. 33, 127–131. doi: 10.1080/17404622.2017.1401730
Butterworth, S. E., Giuliano, T. A., White, J., Cantu, L., and Fraser, K. C. (2019). Sender gender influences emoji interpretation in text messages. Front. Psychol. 10:784. doi: 10.3389/fpsyg.2019.00784
Cahyaningtyas, R. M., Kusumaningrum, R., Sutikno, S, Riyanto, D. E., and IEEE. (2017). “Emotion detection of tweets in Indonesian language using LDA and expression symbol conversion,” in Paper Presented at the 2017 1st International Conference on Informatics and Computational Sciences (ICICoS) (Semarang). doi: 10.1109/ICICOS.2017.8276371
Cappallo, S., Svetlichnaya, S., Garrigues, P., Mensink, T., and Snoek, C. G. M. (2019). New modality: emoji challenges in prediction, anticipation, and retrieval. IEEE Trans. Multimedia 21, 402–415. doi: 10.1109/TMM.2018.2862363
Carvalho, P., Sarmento, L., Silva, M. J., and De Oliveira, E. (2009). “Clues for detecting irony in user-generated contents: oh…!! it's so easy,” in Paper Presented at the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion (Hong Kong). doi: 10.1145/1651461.1651471
Chairunnisa, S., and Benedictus, A. (2017). Analysis of emoji and emoticon usage in interpersonal communication of Blackberry messenger and WhatsApp application user. Int. J. Soc. Sci. Manage. 4, 120–126. doi: 10.3126/ijssm.v4i2.17173
Chao, C., He, X., and Fu, Z. (2019). “Emo-view: convey the emotion of the back-seat passenger with an emoji in rear-view mirror to the driver,” in Paper Presented at the International Conference on Human-Computer Interaction. doi: 10.1007/978-3-030-22580-3_9
Chen, Y., Yuan, J., You, Q., and Luo, J. (2018). “Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM,” in Paper Presented at the 2018 ACM Multimedia Conference on Multimedia Conference. doi: 10.1145/3240508.3240533
Chen, Z., Lu, X., Ai, W., Li, H., Mei, Q., and Liu, X. (2018). “Through a gender lens: learning usage patterns of emojis from large-scale android users,” in Paper Presented at the 2018 World Wide Web Conference on World Wide Web (Lyon). doi: 10.1145/3178876.3186157
Cho, K.-L. (2016). The differences of emoticon use and its effects depending upon problem types and discussion message types in the process of online problem-solving discussions. J. Educ. Technol. 32, 355–390. doi: 10.17232/KSET.32.2.355
Cramer, H., Juan, P. D., and Tetreault, J. (2016). “Sender-intended functions of emojis in US messaging,” in Paper Presented at the International Conference on Human-Computer Interaction With Mobile Devices & Services. doi: 10.1145/2935334.2935370
Das, G., Wiener, H. J. D., and Kareklas, I. (2019). To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising. J. Bus. Res. 96, 147–156. doi: 10.1016/j.jbusres.2018.11.007
Donato, G., and Paggio, P. (2017). “Investigating redundancy in emoji use: study on a twitter based corpus,” in Paper Presented at the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W17-5216
Dunlap, J. C., Bose, D., Lowenthal, P. R., York, C. S., Atkinson, M., and Murtagh, J. (2016). “Chapter 8 – What sunshine is to flowers : a literature review on the use of emoticons to support online learning,” in Emotions Technology Design & Learning, 163–182. doi: 10.1016/B978-0-12-801856-9.00008-6
Esposito, G., Hernã, N. P., Van, B. R., and Vila, J. (2017). Nudging to prevent the purchase of incompatible digital products online: an experimental study. PLoS ONE 12:e0173333. doi: 10.1371/journal.pone.0173333
Fane, J., MacDougall, C., Jovanovic, J., Redmond, G., and Gibbs, L. (2018). Exploring the use of emoji as a visual research method for eliciting young children's voices in childhood research. Early Child Dev. Care 188, 359–374. doi: 10.1080/03004430.2016.1219730
Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., and Lehmann, S. (2017). “Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm,” in Paper Presented at the 2017 Conference on Empirical Methods in Natural Language Processing (Copenhagen). doi: 10.18653/v1/D17-1169
Fernandez-Gavilanes, M., Juncal-Martinez, J., Garcia-Mendez, S., Costa-Montenegro, E., and Javier Gonzalez-Castano, F. (2018). Creating emoji lexica from unsupervised sentiment analysis of their descriptions. Expert Syst. Appl. 103, 74–91. doi: 10.1016/j.eswa.2018.02.043
Gallo, K. E., Swaney-Stueve, M., and Chambers, D. H. (2017). A focus group approach to understanding food-related emotions with children using words and emojis. J. Sens. Stud. 32:e12264. doi: 10.1111/joss.12264
Ganster, T., Eimler, S. C., and Krämer, N. C. (2012). Same same but different!? The differential influence of smilies and emoticons on person perception. Cyberpsychol. Behav. Soc. Netw.15, 226–230. doi: 10.1089/cyber.2011.0179
Gaspar, R., Pedro, C., Panagiotopoulos, P., and Seibt, B. (2016). Beyond positive or negative: qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput. Hum. Behav. 56, 179–191. doi: 10.1016/j.chb.2015.11.040
Gaube, S., Tsivrikos, D., Dollinger, D., and Lermer, E. (2018). How a smiley protects health: a pilot intervention to improve hand hygiene in hospitals by activating injunctive norms through emoticons. PLoS ONE 13:e0197465. doi: 10.1371/journal.pone.0197465
Gibson, W., Huang, P., and Yu, Q. (2018). Emoji and communicative action: the semiotics, sequence and gestural actions of ‘face covering hand'. Discourse Context Media 26, 91–99. doi: 10.1016/j.dcm.2018.05.005
Guthier, B., Ho, K., and El Saddik, A. (2017). “Language-independent data set annotation for machine learning-based sentiment analysis,” in Paper Presented at the Systems, Man, and Cybernetics (SMC), 2017 IEEE International Conference on. doi: 10.1109/SMC.2017.8122930
Hall, J. A., and Pennington, N. (2013). Self-monitoring, honesty, and cue use on Facebook: the relationship with user extraversion and conscientiousness. Comput. Hum. Behav. 29, 1556–1564. doi: 10.1016/j.chb.2013.01.001
Hayati, S. A., and Muis, A. O. (2019). “Analyzing incorporation of emotion in emoji prediction,” in Paper Presented at the Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W19-1311
Herring, S. C., and Dainas, A. R. (2018). “Receiver interpretations of emoji functions: a gender perspective,” in Paper Presented at the 1st International Workshop on Emoji Understanding and Applications in Social Media (Emoji2018) (Stanford).
Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., and Kaymak, U. (2013). “Exploiting emoticons in sentiment analysis,” in Paper Presented at the 28th Annual ACM Symposium on Applied Computing. doi: 10.1145/2480362.2480498
Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., and Caldara, R. (2009). Cultural confusions show that facial expressions are not universal. Curr. Biol. 19, 1543–1548. doi: 10.1016/j.cub.2009.07.051
Jaeger, S. R., and Ares, G. (2017). Dominant meanings of facial emoji: insights from Chinese consumers and comparison with meanings from internet resources. Food Qual. Prefer. 62, 275–283. doi: 10.1016/j.foodqual.2017.04.009
Jaeger, S. R., Lee, S. M., Kim, K.-O., Chheang, S. L., Jin, D., and Ares, G. (2017a). Measurement of product emotions using emoji surveys: case studies with tasted foods and beverages. Food Qual. Prefer. 62, 46–59. doi: 10.1016/j.foodqual.2017.05.016
Jaeger, S. R., Roigard, C. M., and Ares, G. (2018a). Measuring consumers' product associations with emoji and emotion word questionnaires: case studies with tasted foods and written stimuli. Food Res. Int. 111, 732–747. doi: 10.1016/j.foodres.2018.04.010
Jaeger, S. R., Roigard, C. M., Jin, D., Vidal, L., and Ares, G. (2019). Valence, arousal and sentiment meanings of 33 facial emoji: insights for the use of emoji in consumer research. Food Res. Int. 119, 895–907. doi: 10.1016/j.foodres.2018.10.074
Jaeger, S. R., Vidal, L., Kam, K., and Ares, G. (2017b). Can emoji be used as a direct method to measure emotional associations to food names? Preliminary investigations with consumers in USA and China. Food Qual. Prefer. 56, 38–48. doi: 10.1016/j.foodqual.2016.09.005
Jaeger, S. R., Xia, Y., Lee, P.-Y., Hunter, D. C., Beresford, M. K., and Ares, G. (2018b). Emoji questionnaires can be used with a range of population segments: findings relating to age, gender and frequency of emoji/emoticon use. Food Qual. Prefer. 68, 397–410. doi: 10.1016/j.foodqual.2017.12.011
Jiang, F., Liu, Y.-Q., Luan, H.-B., Sun, J.-S., Zhu, X., Zhang, M., et al. (2015). Microblog sentiment analysis with emoticon space model. J. Comput. Sci. Technol. 30, 1120–1129. doi: 10.1007/s11390-015-1587-1
Kaneko, D., Toet, A., Ushiama, S., Brouwer, A.-M., Kallen, V., and van Erp, J. B. F. (2019). EmojiGrid: a 2D pictorial scale for cross-cultural emotion assessment of negatively and positively valenced food. Food Res. Int. 115, 541–551. doi: 10.1016/j.foodres.2018.09.049
Kaye, L. K., Wall, H. J., and Malone, S. A. (2016). “Turn that frown upside-down”: a contextual account of emoticon usage on different virtual platforms. Comput. Hum. Behav. 60, 463–467. doi: 10.1016/j.chb.2016.02.088
Kelly, R., and Watts, L. (2015). “Characterising the inventive appropriation of emoji as relationally meaningful in mediated close personal relationships,” in Paper Presented at the Experiences of Technology Appropriation: Unanticipated Users, Usage, Circumstances, and Design (Oslo).
Khandekar, S., Higgs, J., Bian, Y., Ryu, C. W., Talton, J. O., Kumar, R., et al. (2019). “Opico: a study of emoji-first communication in a mobile social app,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316547
Kim, J.-G., Gong, T., Huang, E., Kim, J., Lee, S.-J., Kim, B., et al. (2019). “Bringing context into emoji recommendations,” in Paper Presented at the Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services. doi: 10.1145/3307334.3328601
Kimura, M., and Katsurai, M. (2017). “Automatic construction of an emoji sentiment lexicon,” in Paper Presented at the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. doi: 10.1145/3110025.3110139
Kraus, L., Schmidt, R., Walch, M., Schaub, F., and Moeller, S. (2017). “On the use of emojis in mobile authentication,” in Paper Presented at the International Conference on Ict Systems Security & Privacy Protection (Cham: Springer) doi: 10.1007/978-3-319-58469-0_18
LeCompte, T., and Chen, J. (2017). “Sentiment analysis of tweets including emoji data,” in Paper Presented at the 2017 International Conference on Computational Science and Computational Intelligence. doi: 10.1109/CSCI.2017.137
Lee, J., Li, J., Mina, A. X., and ACM. (2019). “Hanmoji: what Chinese characters and emoji reveal about each other,” in Paper Presented at the Companion of the World Wide Web Conference. doi: 10.1145/3308560.3316543
Lee, J. Y., Hong, N., Kim, S., Oh, J., and Lee, J. (2016). “Smiley face: why we use emoticon stickers in mobile messaging,” in Paper Presented at the 18th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct. doi: 10.1145/2957265.2961858
Lee, V., and Wagner, H. (2002). The effect of social presence on the facial and verbal expression of emotion and the interrelationships among emotion components. J. Nonverbal Behav. 26, 3–25. doi: 10.1023/A:1014479919684
Li, W., Chen, Y., Hu, T., and Luo, J. (2018). “Mining the relationship between emoji usage patterns and personality,” in Paper Presented at the Twelfth International AAAI Conference on Web and Social Media.
Lima, M., de Alcantara, M., Martins, I. B. A., Ares, G., and Deliza, R. (2019). Can front-of-pack nutrition labeling influence children's emotional associations with unhealthy food products? An experiment using emoji. Food Res. Int. 120, 217–225. doi: 10.1016/j.foodres.2019.02.027
López, R. P., and Cap, F. (2017). “Did you ever read about frogs drinking coffee? investigating the compositionality of multi-emoji expressions,” in Paper Presented at the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. doi: 10.18653/v1/W17-5215
Marengo, D., Settanni, M., and Giannotta, F. (2019). Development and preliminary validation of an image-based instrument to assess depressive symptoms. Psychiatry Res. 279, 180–185. doi: 10.1016/j.psychres.2019.02.059
Mayank, D., Padmanabhan, K., and Pal, K. (2016). “Multi-sentiment modeling with scalable systematic labeled data generation via Word2Vec clustering,” in Paper Presented at the 2016 IEEE 16th International Conference on Data Mining Workshops (Barcelona). doi: 10.1109/ICDMW.2016.0139
Moreno-Sandoval, L. G., Sanchez-Barriga, C., Espindola Buitrago, K., Pomares-Quimbaya, A., and Carlos Garcia, J. (2018). “Spanish Twitter data used as a source of information about consumer food choice,” in Machine Learning and Knowledge Extraction, Cd-Make 2018, Vol. 11015, eds A. Holzinger, P. Kieseberg, A. M. Tjoa, and E. Weippl, 134–146. doi: 10.1007/978-3-319-99740-7_9
Na'aman, N., Provenza, H., and Montoya, O. (2017). “Varying linguistic purposes of emoji in (twitter) context,” in Paper Presented at the ACL 2017, Student Research Workshop. doi: 10.18653/v1/P17-3022
Njenga, K. (2018). “Social media information security threats: anthropomorphic emoji analysis on social engineering,” in Paper Presented at the IT Convergence and Security 2017 (Seoul). doi: 10.1007/978-981-10-6454-8_24
Perry, M. S., and Werner-Wilson, R. J. (2011). Couples and computer-mediated communication: a closer look at the affordances and use of the channel. Fam. Consum. Sci. Res. J. 40, 120–134. doi: 10.1111/j.1552-3934.2011.02099.x
Phan, W. M. J., Amrhein, R., Rounds, J., and Lewis, P. (2019). Contextualizing interest scales with emojis: implications for measurement and validity. J. Career Assessm. 27, 114–133. doi: 10.1177/1069072717748647
Phand, S. A., Chakkarwar, V. A., and IEEE. (2018). “Enhanced sentiment classification using geo location tweets,” in Paper Presented at the 2018 Second International Conference on Inventive Communication and Computational Technologies (Coimbatore).
Prada, M., Rodrigues, D. L., Garrido, M. V., Lopes, D., Cavalheiro, B., and Gaspar, R. (2018). Motives, frequency and attitudes toward emoji and emoticon use. Telematics Inform. 35, 1925–1934. doi: 10.1016/j.tele.2018.06.005
Prasad, A. G., Sanjana, S., Bhat, S. M., Harish, B. S., and IEEE. (2017). “Sentiment analysis for sarcasm detection on streaming short text data,” in Paper Presented at the 2017 2nd International Conference on Knowledge Engineering and Applications (ICKEA). doi: 10.1109/ICKEA.2017.8169892
Rathan, M., Hulipalled, V. R., Murugeshwari, P., and Sushmitha, H. (2017). “Every post matters: a survey on applications of sentiment analysis in social media,” in Paper Presented at the Smart Technologies for Smart Nation (SmartTechCon), 2017 International Conference On (Bangalore). doi: 10.1109/SmartTechCon.2017.8358463
Rathan, M., Hulipalled, V. R., Venugopal, K. R., and Patnaik, L. M. (2018). Consumer insight mining: aspect based Twitter opinion mining of mobile phone reviews. Appl. Soft Comput. 68, 765–773. doi: 10.1016/j.asoc.2017.07.056
Redmond, M., Salesi, S., Cosma, G., and IEEE. (2017). “A novel approach based on an extended cuckoo search algorithm for the classification of tweets which contain emoticon and emoji,” in Paper Presented at the 2017 2nd International Conference on Knowledge Engineering and Applications (London). doi: 10.1109/ICKEA.2017.8169894
Rodrigues, D., Prada, M., Rui, G., Garrido, M. V., and Lopes, D. (2017). Lisbon emoji and emoticon database (LEED): norms for emoji and emoticons in seven evaluative dimensions. Behav. Res. Methods 50, 1–14. doi: 10.3758/s13428-017-0878-6
Sadiq, M., and Shahida, IEEE. (2019). “Learning Pakistani culture through the namaz emoji,” in Paper Presented at the 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies. doi: 10.1109/ICOMET.2019.8673479
Sari, Y. A., Ratnasari, E. K., Mutrofin, S., and Arifin, A. Z. (2014). User emotion identification in Twitter using specific features: hashtag, emoji, emoticon, and adjective term. Jurnal Ilmu Komputer dan Informasi 7, 18–23. doi: 10.21609/jiki.v7i1.252
Schouteten, J. J., Verwaeren, J., Gellynck, X., and Almli, V. L. (2019). Comparing a standardized to a product-specific emoji list for evaluating food products by children. Food Qual. Prefer. 72, 86–97. doi: 10.1016/j.foodqual.2018.09.007
Siegel, R. M., Anneken, A., Duffy, C., Simmons, K., Hudgens, M., Lockhart, M. K., et al. (2015). Emoticon use increases plain milk and vegetable purchase in a school cafeteria without adversely affecting total milk purchase. Clin. Ther. 37, 1938–1943. doi: 10.1016/j.clinthera.2015.07.016
Singh, A., Blanco, E., and Jin, W. (2019). “Incorporating emoji descriptions improves tweet classification,” in Paper Presented at the Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). doi: 10.18653/v1/N19-1214
Tan, L., Hui, J. T., Lai, K. S., and Low, J. A. (2018). Sensitivity and specificity analysis: use of emoticon for screening of depression in elderly in Singapore. J. Am. Psychiatr. Nurses Assoc 24, 452–456. doi: 10.1177/1078390318766665
Tauch, C., and Kanjo, E. (2016). “The roles of emojis in mobile phone notifications,” in Paper Presented at the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct (Heidelberg). doi: 10.1145/2968219.2968549
Thompson, D., Mackenzie, I. G., Leuthold, H., and Filik, R. (2016). Emotional responses to irony and emoticons in written language: evidence from EDA and facial EMG. Psychophysiology 53, 1054–1062. doi: 10.1111/psyp.12642
Thomson, S., Kluftinger, E., and Wentland, J. (2018). Are you fluent in sexual emoji?(sic): exploring the use of emoji in romantic and sexual contexts. Can. J. Hum. Sex. 27, 226–234. doi: 10.3138/cjhs.2018-0020
Tigwell, G. W., and Flatla, D. R. (2016). “Oh that's what you meant!: reducing emoji misunderstanding,” in Paper Presented at the 18th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct (Florence). doi: 10.1145/2957265.2961844
Tossell, C. C., Kortum, P., Shepard, C., Barg-Walkow, L. H., Rahmati, A., and Zhong, L. (2012). A longitudinal study of emoticon use in text messaging from smartphones. Comput. Hum. Behav. 28, 659–663. doi: 10.1016/j.chb.2011.11.012
Troiano, G., and Nante, N. (2018). Emoji: what does the scientific literature say about them?-A new way to communicate in the 21th century. J. Hum. Behav. Soc. Environ. 28, 528–533. doi: 10.1080/10911359.2018.1437103
Vanin, A. A., Freitas, L. A., Vieira, R., and Bochernitsan, M. (2013). “Some clues on irony detection in tweets” in Paper Presented at the 22nd International Conference on World Wide Web. doi: 10.1145/2487788.2488012
Wall, H. J., Kaye, L. K., and Malone, S. A. (2016). An exploration of psychological factors on emoticon usage and implications for judgement accuracy. Comput. Hum. Behav. 62, 70–78. doi: 10.1016/j.chb.2016.03.040
Wang, W., Lu, C., Thirunarayan, K., and Sheth, A. P. (2012). “Harnessing Twitter “big data” for automatic emotion identification,” in Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom) (Amsterdam: IEEE), 587–592. doi: 10.1109/SocialCom-PASSAT.2012.119
Wang, Z., Cui, X., Gao, L., Yin, Q., Ke, L., and Zhang, S. (2016). A hybrid model of sentimental entity recognition on mobile social media. EURASIP J. Wirel Commun. Netw. 2016:253. doi: 10.1186/s13638-016-0745-7
Wijeratne, S., Balasuriya, L., Sheth, A., Doran, D., and ACM. (2017). “A semantics-based measure of emoji similarity,” in Paper Presented at the 2017 IEEE/WIC/ACM International Conference on Web Intelligence. doi: 10.1145/3106426.3106490
Xuan, L., Wei, A., Liu, X., Qian, L., Ning, W., Gang, H., et al. (2016). “Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users,” in Paper Presented at the ACM International Joint Conference on Pervasive & Ubiquitous Computing (Heidelberg).
Zanzotto, F. M., and Santilli, A. (2018). “SyntNN at SemEval-2018 task 2: is syntax useful for emoji prediction? embedding syntactic trees in multi layer perceptrons,” in Paper Presented at the Proceedings of The 12th International Workshop on Semantic Evaluation. doi: 10.18653/v1/S18-1076
Keywords: emoji, communication, emotion expression, semantic expression, systematic review
Citation: Bai Q, Dan Q, Mu Z and Yang M (2019) A Systematic Review of Emoji: Current Research and Future Perspectives. Front. Psychol. 10:2221. doi: 10.3389/fpsyg.2019.02221
Received: 14 June 2019; Accepted: 17 September 2019;
Published: 15 October 2019.
Edited by:Roberto Therón, University of Salamanca, Spain
Reviewed by:Claus-Christian Carbon, University of Bamberg, Germany
Davide Marengo, University of Turin, Italy
Copyright © 2019 Bai, Dan, Mu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Qiyu Bai, firstname.lastname@example.org
†These authors have contributed equally to this work