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Sentiment of Emojis

Author

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  • Petra Kralj Novak
  • Jasmina Smailović
  • Borut Sluban
  • Igor Mozetič

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.

Suggested Citation

  • Petra Kralj Novak & Jasmina Smailović & Borut Sluban & Igor Mozetič, 2015. "Sentiment of Emojis," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
  • Handle: RePEc:plo:pone00:0144296
    DOI: 10.1371/journal.pone.0144296
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    Cited by:

    1. Peter Gabrovšek & Darko Aleksovski & Igor Mozetič & Miha Grčar, 2017. "Twitter sentiment around the Earnings Announcement events," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    2. Jennifer Fane & Colin MacDougall & Jessie Jovanovic & Gerry Redmond & Lisa Gibbs, 2020. "Preschool Aged Children’s Accounts of their Own Wellbeing: are Current Wellbeing Indicators Applicable to Young Children?," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 13(6), pages 1893-1920, December.
    3. Tiziana CARPI & Airo HINO & Stefano Maria IACUS & Giuseppe PORRO, 2022. "A Japanese Subjective Well-Being Indicator Based on Twitter Data [‘Collective Smile: Measuring Societal Happiness from Geolocated Images’]," Social Science Japan Journal, University of Tokyo and Oxford University Press, vol. 25(2), pages 273-296.
    4. Nader Mahmoudi & Łukasz P. Olech & Paul Docherty, 2022. "A comprehensive study of domain-specific emoji meanings in sentiment classification," Computational Management Science, Springer, vol. 19(2), pages 159-197, June.
    5. Béatrice BOULU-RESHEF & Catherine BRUNEAU & Maxime NICOLAS & Thomas RENAULT, 2022. "An Experimental Analysis of Investor Sentiment," LEO Working Papers / DR LEO 2940, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    6. Sarah N. Alyami & Sunday O. Olatunji, 2020. "Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-13, April.
    7. Anand, Abhinav & Pathak, Jalaj, 2022. "The role of Reddit in the GameStop short squeeze," Economics Letters, Elsevier, vol. 211(C).
    8. Klostermann, Jan & Plumeyer, Anja & Böger, Daniel & Decker, Reinhold, 2018. "Extracting brand information from social networks: Integrating image, text, and social tagging data," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 538-556.
    9. Carlos Henríquez Miranda & German Sanchez-Torres & Dixon Salcedo, 2023. "Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture," Data, MDPI, vol. 8(6), pages 1-18, May.
    10. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    11. Oriol J. Bosch & Melanie Revilla, 2021. "Using emojis in mobile web surveys for Millennials? A study in Spain and Mexico," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 39-61, February.
    12. Mohammed Talha Alam & Shahab Saquib Sohail & Syed Ubaid & Shakil & Zafar Ali & Mohammad Hijji & Abdul Khader Jilani Saudagar & Khan Muhammad, 2022. "It’s Your Turn, Are You Ready to Get Vaccinated? Towards an Exploration of Vaccine Hesitancy Using Sentiment Analysis of Instagram Posts," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    13. Adolfina Pérez Garcias & Gemma Tur & Antònia Darder Mesquida & Victoria I. Marín, 2020. "Reflexive Skills in Teacher Education: A Tweet a Week," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    14. Maria Zunally Rapada & Derrick Ethelbhert Yu & Krista Danielle Yu, 2021. "Do Social Media Posts Influence Consumption Behavior towards Plastic Pollution?," Sustainability, MDPI, vol. 13(22), pages 1-18, November.
    15. Das, Gopal & Wiener, Hillary J.D. & Kareklas, Ioannis, 2019. "To emoji or not to emoji? Examining the influence of emoji on consumer reactions to advertising," Journal of Business Research, Elsevier, vol. 96(C), pages 147-156.
    16. Benhoumane Ahmed, 2020. "What Makes You "Like", "Retweet" or "Comment" a Fundraising Content on Social Media? Exploring the Characteristics of Fundraising Messages on Social Networks [Qu'est-c," Post-Print hal-03390922, HAL.
    17. Ganga S. Urumutta Hewage & Yue Liu & Ze Wang & Huifang Mao, 2021. "Consumer responses toward symmetric versus asymmetric facial expression emojis," Marketing Letters, Springer, vol. 32(2), pages 219-230, June.
    18. Salim Moussa, 2021. "Measuring brand personality using emoji: findings from Mokken scaling," Journal of Brand Management, Palgrave Macmillan, vol. 28(2), pages 116-132, March.
    19. J Jobu Babin, 2020. "Linguistic signaling, emojis, and skin tone in trust games," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-14, June.
    20. Jiamin Pei & Le Cheng, 2022. "Deciphering emoji variation in courts: a social semiotic perspective," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-8, December.

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