The Effects of Emoji in Sentiment Analysis

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Abstract: This study investigates the usage of Emoji characters on social networks and the effects of Emoji in  text  mining and sentiment  analysis. As  it provides live access  to  text  based  public opinions,  we  chose Twitter  as our  information  source  in  our  analysis.  We  collected  text  data  for  some  global  positive  and negative events to analyze the impact of Emoji characters in sentiment analysis. In our analysis, we noticed that  the  utilization  of  Emoji  characters  in  sentiment  analysis  results  in  higher  sentiment  scores. Furthermore, we observed that the usage of Emoji characters in sentiment analysis appeared to have higher impact on overall sentiments of the positive opinions in comparison to the negative opinions.    Key words: Emoji, opinion mining, sentiment analysis, twitter.
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https://www.researchgate.net/publication/320446679_The_Effects_of_Emoji_in_Sentiment_Analysis

Emoji Sentiment Ranking

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Abstract
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.
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http://kt.ijs.si/data/Emoji_sentiment_ranking/index.html