Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test multiple embedding models to measure emoji similarity. To evaluate our work, we create a new dataset called EmoSim508, which assigns human-annotated semantic similarity scores to a set of 508 carefully selected emoji pairs. After validation with EmoSim508, we present a real-world use-case of our emoji embedding models using a sentiment analysis task and show that our models outperform the previous best-performing emoji embedding model on this task. The EmoSim508 dataset and our emoji embedding models are publicly released with this paper and can be downloaded from http: emojinet.knoesis.org .
While emoji were introduced in the late 1990s, their use and popularity was limited until the Unicode Consortium started to standardize emoji symbols in 2009 @cite_15 . Major mobile phone manufactures such as Apple, Google, Microsoft, and Samsung then began supporting emoji in their device operating systems between 2011 and 2013, which boosted emoji adoption around the world @cite_27 . Early research on emoji was focused on understanding the role of emoji in computer-mediated communication. Kelly et al studied how people in close relationships use emoji in their communications and reported that they use emoji as a way of making their conversations playful @cite_34 . Pavalanathan et al studied how Twitter users adopt emoji and reported that Twitter users prefer emoji over emoticons @cite_10 . Researchers have also studied how emoji usage and interpretation differ across mobile and computer platforms @cite_35 @cite_9 @cite_23 , geographies @cite_30 , and across languages @cite_19 where many others have used emoji as features in their learning algorithms for problems such as emoji-based search @cite_22 , sentiment analysis @cite_29 , emotion analysis @cite_13 , and Twitter profile classification @cite_25 @cite_5 .
@cite_30 @cite_35 @cite_13 @cite_22 @cite_9 @cite_29 @cite_19 @cite_27 @cite_23 @cite_5 @cite_15 @cite_34 @cite_10 @cite_25
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There is a new generation of emoticons, called emojis, that is increasingly being used in mobile communications and social media. In the past two years, over ten billion emojis were used on Twitter. Emojis are Unicode graphic symbols, used as a shorthand to express concepts and ideas. In contrast to the small number of well-known emoticons that carry clear emotional contents, there are hundreds of emojis. But what are their emotional contents? We provide the first emoji sentiment lexicon, called the Emoji Sentiment Ranking, and draw a sentiment map of the 751 most frequently used emojis. The sentiment of the emojis is computed from the sentiment of the tweets in which they occur. We engaged 83 human annotators to label over 1.6 million tweets in 13 European languages by the sentiment polarity (negative, neutral, or positive). About 4 of the annotated tweets contain emojis. The sentiment analysis of the emojis allows us to draw several interesting conclusions. It turns out that most of the emojis are positive, especially the most popular ones. The sentiment distribution of the tweets with and without emojis is significantly different. The inter-annotator agreement on the tweets with emojis is higher. Emojis tend to occur at the end of the tweets, and their sentiment polarity increases with the distance. We observe no significant differences in the emoji rankings between the 13 languages and the Emoji Sentiment Ranking. Consequently, we propose our Emoji Sentiment Ranking as a European language-independent resource for automated sentiment analysis. Finally, the paper provides a formalization of sentiment and a novel visualization in the form of a sentiment bar. Emoji are commonly used in modern text communication. However, as graphics with nuanced details, emoji may be open to interpretation. Emoji also render differently on different viewing platforms (e.g., Apple’s iPhone vs. Google’s Nexus phone), potentially leading to communication errors. We explore whether emoji renderings or differences across platforms give rise to diverse interpretations of emoji. Through an online survey, we solicit people’s interpretations of a sample of the most popular emoji characters, each rendered for multiple platforms. Both in terms of sentiment and semantics, we analyze the variance in interpretation of the emoji, quantifying which emoji are most (and least) likely to be misinterpreted. In cases in which participants rated the same emoji rendering, they disagreed on whether the sentiment was positive, neutral, or negative 25 of the time. When considering renderings across platforms, these disagreements only increase. Overall, we find significant potential for miscommunication, both for individual emoji renderings and for different emoji renderings across platforms. Emoji are a contemporary and extremely popular way to enhance electronic communication. Without rigid semantics attached to them, emoji symbols take on different meanings based on the context of a message. Thus, like the word sense disambiguation task in natural language processing, machines also need to disambiguate the meaning or ‘sense’ of an emoji. In a first step toward achieving this goal, this paper presents EmojiNet, the first machine readable sense inventory for emoji. EmojiNet is a resource enabling systems to link emoji with their context-specific meaning. It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date. The paper discusses its construction, evaluates the automatic resource creation process, and presents a use case where EmojiNet disambiguates emoji usage in tweets. EmojiNet is available online for use at http: emojinet.knoesis.org. Emoji provide a way to express nonverbal conversational cues in computer-mediated communication. However, people need to share the same understanding of what each emoji symbolises, otherwise communication can breakdown. We surveyed 436 people about their use of emoji and ran an interactive study using a two-dimensional emotion space to investigate (1) the variation in people's interpretation of emoji and (2) their interpretation of corresponding Android and iOS emoji. Our results show variations between people's ratings within and across platforms. We outline our solution to reduce misunderstandings that arise from different interpretations of emoji.