Sentiment of Emojis

By Petra Kralj Novak, Jasmina Smailovic, Borut Sluban, Igor Mozetic. 2015

915 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Human-Computer Interaction

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Citations in the corpus

"Blissfully Happy" or "Ready toFight": Varying Interpretations of Emoji (2016, background): “Emoji are often described as a successor to emoticons (e.g., Novak et al. 2015), and Pavalanathan and Eisenstein (2016) found that while emoticons are decreasing in popularity on Twitter, emoji are increasing in popularity and seem to be replacing, not complementing, emoticons.”
emoji2vec: Learning Emoji Representations from their Description (2016, methodology): “We use the recent dataset by Kralj Novak et al. (2015), which consists of over 67k English tweets labelled manually for positive, neutral, or negative sentiment.”
SemEval 2018 Task 2: Multilingual Emoji Prediction (2018, background): “In general, however, effectively predicting the emoji associated with a piece of content may help to improve different NLP tasks (Novak et al., 2015), such as information retrieval, generation of emoji-enriched social media content, suggestion of emojis when writing text messages or sharing…”
Emoticons vs. Emojis on Twitter: A Causal Inference Approach (2015, background): “Novak et al. (2015) developed a sentiment lexicon for emojis using their usage in tweets.”
Reusable workflows for gender prediction (2018, background): “Emoji Sentiment widget (Novak et al., 2015) takes a column of the corpus containing texts as an input and returns a sentiment score for every text.”
Are Emojis Creating a New or Old Visual Language for New Generations? A Socio-semiotic Study (2016, background): “These are keyboard symbols which are combined to make pictures, for example “:)” which represents a smiley face, and “ 3” which represents a heart (Novak et al, 2015).”