research-article

Exploring (Dis-)Similarities in Emoji-Emotion Association on Twitter and Weibo

Authors:

Sharath Guntuku,

Vinit Jakhetiya,

Lyle UngarAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 461 - 467

https://doi.org/10.1145/3308560.3316546

Published: 13 May 2019 Publication History

Abstract

Emojis have gained widespread acceptance, globally and cross-culturally. However, Emoji use may also be nuanced due to differences across cultures, which can play a significant role in shaping emotional life. In this paper, we a) present a methodology to learn latent emotional components of Emojis, b) compare Emoji-Emotion associations across cultures, and c) discuss how they may reflect emotion expression in these platforms. Specifically, we learn vector space embeddings with more than 100 million posts from China (Sina Weibo) and the United States (Twitter), quantify the association of Emojis with 8 basic emotions, demonstrate correlation between visual cues and emotional valence, and discuss pairwise similarities between emotions. Our proposed Emoji-Emotion visualization pipeline for uncovering latent emotional components can potentially be used for downstream applications such as sentiment analysis and personalized text recommendations.

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Cited By

Qi Y(2024)Research on Speech Recognition Methods with Emotional Description2024 International Conference on Artificial Intelligence and Power Systems (AIPS)10.1109/AIPS64124.2024.00056(243-248)Online publication date: 19-Apr-2024
https://doi.org/10.1109/AIPS64124.2024.00056
Kim JKang MSeo BHong JKim S(2023)Effective Emoticon Suggestion Technique Based on Active Emotional Input Using Facial Expressions and Heart Rate SignalsSensors10.3390/s2309446023:9(4460)Online publication date: 3-May-2023
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Index Terms

Exploring (Dis-)Similarities in Emoji-Emotion Association on Twitter and Weibo

Index terms have been assigned to the content through auto-classification.

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WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

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Cited By

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https://doi.org/10.1109/AIPS64124.2024.00056
Kim JKang MSeo BHong JKim S(2023)Effective Emoticon Suggestion Technique Based on Active Emotional Input Using Facial Expressions and Heart Rate SignalsSensors10.3390/s2309446023:9(4460)Online publication date: 3-May-2023
https://doi.org/10.3390/s23094460
Lim HCosley DFussell S(2022)Understanding Cross-lingual Pragmatic Misunderstandings in Email CommunicationProceedings of the ACM on Human-Computer Interaction10.1145/35129766:CSCW1(1-32)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3512976
Wang JGui TCheng MWu XRuan RDu M(2022)A survey on emotional visualization and visual analysisJournal of Visualization10.1007/s12650-022-00872-526:1(177-198)Online publication date: 10-Sep-2022
https://doi.org/10.1007/s12650-022-00872-5
Xu XManrique RPereira Nunes BConlan OHerder E(2021)RIP Emojis and Words to Contextualize Mourning on TwitterProceedings of the 32nd ACM Conference on Hypertext and Social Media10.1145/3465336.3475100(257-263)Online publication date: 30-Aug-2021
https://dl.acm.org/doi/10.1145/3465336.3475100
Zhao GLiu ZChao YQian X(2021)CAPER: Context-Aware Personalized Emoji RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.296697133:9(3160-3172)Online publication date: 4-Aug-2021
https://dl.acm.org/doi/10.1109/TKDE.2020.2966971
Hickman LThapa STay LCao MSrinivasan P(2020)Text Preprocessing for Text Mining in Organizational Research: Review and RecommendationsOrganizational Research Methods10.1177/109442812097168325:1(114-146)Online publication date: 23-Nov-2020
https://doi.org/10.1177/1094428120971683
Ding FKang XNishide SGuan ZRen F(2020)A fusion model for multi-label emotion classification based on BERT and topic clusteringInternational Symposium on Artificial Intelligence and Robotics 202010.1117/12.2579255(36)Online publication date: 12-Oct-2020
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Sutherland KSutherland K(2020)Writing for Social MediaStrategic Social Media Management10.1007/978-981-15-4658-7_14(287-324)Online publication date: 22-Dec-2020
https://doi.org/10.1007/978-981-15-4658-7_14

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