E mo T ag1200: Understanding the Association between Emojis and Emotions (2020)

Abstract: Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role. One conspicuous aspect of emojis is their use to convey affect in ways that may otherwise be non-trivial to achieve. In this paper, we seek to explore the connection between emojis and emotions by means of a new dataset consisting of human-solicited association ratings. We additionally conduct experiments to assess to what extent such associations can be inferred from existing data in an unsupervised manner. Our experiments show that this succeeds when high-quality word-level information is available.

## introduction people increasingly rely on digital channels such as mobile instant messaging apps to communicate with their friends, families, colleagues, and communities. along with this rapid shift in medium, there have been concomitant changes in the way people express themselves in written language @xcite . one notable development has been the emergence of emojis as a new modality, presenting rich possibilities for representation and interaction. emojis have become ubiquitous in social media and in instant messaging, owing in part to their visual appeal and their ease of use compared to typing out full words on mobile devices. however, the rise of emojis also substantially appears to stem from their ability to convey affect @xcite . this is evinced by the fact that the most frequently used emojis are smileys and other facial expression symbols that exhibit a direct connection to emotional expression @xcite . these largely displaced traditional emoticons such as ":-)" and ":)", which as well were chiefly used to convey humor and emotion @xcite , as also reflected in their name, a portmanteau of the words emotion and icon. this mandates additional analysis of the nexus between emojis and emotion. past work has compiled a list of sentiment polarity scores for a set of emojis @xcite . @xcite categorized a set of 15 emojis into 4 different emotion classes, while @xcite used a lexicon-based heuristic to compare connections between emojis and emotions in social media data. several studies have explored the linguistic connection between words and emojis @xcite @xcite . however, previous work has not assessed to what extent humans associate particular emotions with different emojis. in this work, we present emotag1200, a dataset of human ratings of association for a set of 150 popular emojis with regard to 8 different emotions. each of the resulting 1,200 pairs of emojis and emotions has been annotated by 9 human raters on a 5-point scale. the purpose of this endeavor is to measure the degree of emotion that people associate with the use of a given emoji in written expression. as the set of emotions, we consider the eight basic ones in the wheel of emotions by @xcite , i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. the emo-tag1200 dataset as well as additional emoji-related resources are available online foot_0 . we assess the emotion scores of this set of emojis and subsequently study a series of simple unsupervised models to predict such emotion intensity scores automatically. for this, we investigate standard pre-trained vector embedding models, but also consider an emoji-centric corpus consisting of 20.8m tweets to study how it can expose semantic relationships between emotion words and emojis, drawing on additional lexical resources. the results suggest that models drawing on wordlevel emotion intensity information as background knowledge fare better than vanilla vector embedding models. in light of this important role, humans continue to rely extensively on such nonverbal cues in oral forms of linguistic communication. although a person's emotion and mood can to some extent be conveyed by means of suitable content words (e.g., "i am happy to hear that!") or interjections ("wow!"), face-to-face communication has important properties that written communication tends to lack @xcite . these include facial expressions of the aforementioned sort, but also gesture and intonation. in certain problem-solving settings, for instance, face-to-face communication may hence prove more efficient and effective @xcite . accordingly, throughout the history of writing, humans have resorted to surrogate mechanisms to convey emotive signals, attempting to push the boundaries and overcome some of the inherent restrictions of plain written language as a medium, e.g., by means of illustrative embellishments and ornaments @xcite . user studies have shown that images @xcite , color @xcite , and typography @xcite contribute to conveying affect. emoticons. emoticons such as ":-)" and japanese 顔文字 (kaomoji) such as "(ˆˆ)", both composed from regular symbols, have been in use for several decades. early studies focused on the use of emoticons in social media. @xcite proposed a form of distant supervision by using emoticons as noisy labels for twitter sentiment classification. @xcite adopted a similar approach by handpicking smileys and hashtags as tweet labels to train a supervised model to classify the sentiment of tweets. emojis. emoji characters are pictorial, similar to earlier dingbat characters, but also colorful. despite the lexicographic similarity between the two words emoji and emotion, etymologically, the former stems from the japanese words 絵 (e, picture) and 文字 (moji, character). emojis originated in japan in the 1990s and have only recently spread globally. historically, the spread of emojis has been driven in large part by their adoption in popular messaging and social media platforms, which led, among other things, to their inclusion in shift jis, and, subsequently, the unicode standard. nowadays, they are ubiquitous in social media and chat applications, but increasingly also in emails and other digital correspondence. emojis have a number of different roles. @xcite explained how emojis may aid the interlocutor in disambiguating utterances that would otherwise remain ambiguous. one of their principal uses has been to convey emotion, particularly via facial expression emojis, as explained in section 1. in 2015, oxford dictionaries declared the face with tears of joy emoji its word of the year 2015. emojis may also be useful as a more instantaneously and widely recognized form of communicating degrees of satisfaction. @xcite go as far as suggesting them for consideration as possible alternatives to regular likert scales. emoji semantics. the mit deepmoji project @xcite developed a model that recommends emojis given a natural language sentence as input. a deep neural architecture was trained on a collection of 1.2b tweets to learn the sentiment, emotions, and the use of sarcasm in short text. @xcite proposed a method to learn vector space embeddings of emojis using the standard word2vec skip-gram approach, applied to a large collection of tweets. in contrast, @xcite attempted to learn vector embeddings of emojis based on their short descriptions in the unicode standard. emojinet @xcite provides a sense inventory to distinguish different senses of an emoji, drawing on web-crawled emoji definitions and connecting them to word senses from a lexical resource, along with vector representations of context words. the first paper to thoroughly investigate the sen-timent of emojis @xcite proposed a sentiment ranking of 715 emojis on a corpus of 70,000 tweets. this work provides a basis for future research on the logographic usage of emojis in social media. @xcite classify 15 emojis with regard to their sentiment polarity and with regard to 4 emotion classes. for this, they applied a distant supervision technique for a reliable mapping based on manually annotated data. @xcite used a heuristic to observe ties between emojis and emotions in social media data and compared emoji usage on twitter and weibo. their heuristic involves training word vector models and then invoking a word-emotion lexicon to obtain average vectors for 8 emotions. finally, emotag @xcite provides interpretable word vectors that describe words in terms of their association with emojis. these vectors were found to be useful for emotion prediction. @xcite trained a natural language conversation model that accounts for the underlying emotion of utterances by exploiting the existence of emojis as a signal. ## annotation task in order to better study the connection between emojis and emotions, we proceeded to compile a dataset of ratings quantifying the perceived strength of association between emojis and emotions. ## emotion scoring experiments given our manually collected data for 150 emojis, we next consider to what extent simple unsupervised methods and resources correlate with these associations such that they could be used to reproduce such associations automatically in a datadriven manner. the emotag1200 data compiled in section 3, specifically the mean ratings for emojiemotion pairs, serve as the ground truth. ## conclusion the desire to express an emotion is one of the factors that has driven the tremendous proliferation of emojis in interpersonal communication. however, this connection has not been studied in sufficient detail, at the level of individual emojis. in this work, we shed light on this connection by compiling the emotag1200 dataset, which quantifies people's reported association between emojis and emotion. from each of 9 human raters, we solicit 1,200 ratings covering a set of 150 emojis with regard to 8 core emotions from @xcite 's wheel of emotions. this constitutes the first resource of this kind, which we thoroughly analyze and make freely available to enable further research. an important avenue of future work will be to assess to what extent there may be cultural differences in these associations (see discussion in section 3.1). similarly, variation with respect to age and other variables merits further study as well. temporal aspects could be considered in diachronic studies, to account for the fact that emoji use has been evolving. finally, we rely on our annotated data to study how well we can automatically estimate emotional association ratings for a given emoji, considering a series of different baseline methods and resources. our findings suggest that data-driven methods can fare quite well at this if combined with high-quality affective intensity information at the lexical level. hence, we are able to predict high-quality emotion scores for a larger set of emojis. this opens up further research avenues on possible downstream applications exploiting this knowledge. the most obvious use cases are sentiment analysis @xcite , emotion analysis (raji and de melo, 2020), consumer behaviour analytics @xcite , context-sensitive emoji recommendation @xcite , computational social science and public opinion mining @xcite , and user modeling @xcite , but it may also be useful in dialogue systems @xcite , e.g. to detect sarcasm. as emoji use is now ubiquitous on mobile devices and social media, we believe that ultimately any nlp task involving social media text may benefit from such emoji resources.