research-article

Emoji-Based Sentiment Analysis Using Attention Networks

Authors:

Donghong JiAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP), Volume 19, Issue 5

Article No.: 64, Pages 1 - 13

https://doi.org/10.1145/3389035

Published: 01 June 2020 Publication History

Abstract

Emojis are frequently used to express moods, emotions, and feelings in social media. There has been much research on emojis and sentiments. However, existing methods mainly face two limitations. First, they treat emojis as binary indicator features and rely on handcrafted features for emoji-based sentiment analysis. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this article, we investigate a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work. First, it does not need feature engineering. Second, it utilizes the attention approach to model the impact of emojis on text. An evaluation on 10,042 manually labeled Sina Weibo showed that our model achieves much better performance compared with several strong baselines. To facilitate the related research, our corpus will be publicly available at https://github.com/yx100/emoji.

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Cited By

Balaji PAlsid LSagar KPrakasam S(2025)Deep neural network for emojis-based sentiment classificationInternational Journal of Information Technology10.1007/s41870-025-02421-3Online publication date: 27-Jan-2025
https://doi.org/10.1007/s41870-025-02421-3
Tang HTang WZhu DWang SWang YWang L(2024)EMFSA: Emoji-based multifeature fusion sentiment analysisPLOS ONE10.1371/journal.pone.031071519:9(e0310715)Online publication date: 19-Sep-2024
https://doi.org/10.1371/journal.pone.0310715
Lee SKim SChu YChoi JPark EWoo S(2024)EAE-GAN: Emotion-Aware Emoji Generative Adversarial Network for Computational Modeling Diverse and Fine-Grained Human EmotionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332943411:3(3862-3872)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3329434
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Index Terms

Emoji-Based Sentiment Analysis Using Attention Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Sentiment analysis

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Published In

cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 19, Issue 5

September 2020

278 pages

ISSN:2375-4699

EISSN:2375-4702

DOI:10.1145/3403646

Editor:
Imed Zitouni
Microsoft, USA

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Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2020

Online AM: 07 May 2020

Accepted: 01 March 2020

Revised: 01 December 2019

Received: 01 February 2019

Published in TALLIP Volume 19, Issue 5

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Cited By

Balaji PAlsid LSagar KPrakasam S(2025)Deep neural network for emojis-based sentiment classificationInternational Journal of Information Technology10.1007/s41870-025-02421-3Online publication date: 27-Jan-2025
https://doi.org/10.1007/s41870-025-02421-3
Tang HTang WZhu DWang SWang YWang L(2024)EMFSA: Emoji-based multifeature fusion sentiment analysisPLOS ONE10.1371/journal.pone.031071519:9(e0310715)Online publication date: 19-Sep-2024
https://doi.org/10.1371/journal.pone.0310715
Lee SKim SChu YChoi JPark EWoo S(2024)EAE-GAN: Emotion-Aware Emoji Generative Adversarial Network for Computational Modeling Diverse and Fine-Grained Human EmotionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332943411:3(3862-3872)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3329434
Lou YZhou JZhou JJi DZhang Q(2024)Emoji multimodal microblog sentiment analysis based on mutual attention mechanismScientific Reports10.1038/s41598-024-80167-x14:1Online publication date: 26-Nov-2024
https://doi.org/10.1038/s41598-024-80167-x
Xu QJayne CChang V(2024)An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviewsTechnological Forecasting and Social Change10.1016/j.techfore.2024.123326202(123326)Online publication date: May-2024
https://doi.org/10.1016/j.techfore.2024.123326
Bashiri HNaderi H(2024)Comprehensive review and comparative analysis of transformer models in sentiment analysisKnowledge and Information Systems10.1007/s10115-024-02214-366:12(7305-7361)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10115-024-02214-3
Gorai JShaw D(2024)A BERT-encoded ensembled CNN model for suicide risk identification in social media postsNeural Computing and Applications10.1007/s00521-024-09642-w36:18(10955-10970)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09642-w
Cui SThakur NPoon A(2024)Emoji Retrieval from Gibberish or Garbled Social Media Text: A Novel Methodology and a Case StudyHCI International 2024 – Late Breaking Papers10.1007/978-3-031-76806-4_14(170-189)Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1007/978-3-031-76806-4_14
Eljil KNait-Abdesselam FHamouda EHamdi M(2023)Enhancing Sentiment Analysis on Social Media with Novel Preprocessing TechniquesJournal of Advances in Information Technology10.12720/jait.14.6.1206-121314:6(1206-1213)Online publication date: 2023
https://doi.org/10.12720/jait.14.6.1206-1213
Tang TNongpong K(2023)Impact of Emojis in Emotion Analysis on Code-Mixed TextProceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval10.1145/3639233.3639342(25-30)Online publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1145/3639233.3639342
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