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Emoji-Based Sentiment Analysis Using Attention Networks

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

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  • (2024)EMFSA: Emoji-based multifeature fusion sentiment analysisPLOS ONE10.1371/journal.pone.031071519:9(e0310715)Online publication date: 19-Sep-2024
  • (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
  • (2024)Emoji multimodal microblog sentiment analysis based on mutual attention mechanismScientific Reports10.1038/s41598-024-80167-x14:1Online publication date: 26-Nov-2024
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    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
    Issue’s Table of Contents
    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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    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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    Author Tags

    1. Sentiment analysis
    2. attention
    3. deep learning
    4. emoji
    5. social media

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    • National Social Science
    • National Natural Science Foundation
    • Science and Technology
    • Natural Science Foundation

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    Cited By

    View all
    • (2024)EMFSA: Emoji-based multifeature fusion sentiment analysisPLOS ONE10.1371/journal.pone.031071519:9(e0310715)Online publication date: 19-Sep-2024
    • (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
    • (2024)Emoji multimodal microblog sentiment analysis based on mutual attention mechanismScientific Reports10.1038/s41598-024-80167-x14:1Online publication date: 26-Nov-2024
    • (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
    • (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
    • (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
    • (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
    • (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
    • (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
    • (2023)The Role of Emojis in Sentiment Analysis of Financial Microblogs2023 Fourth International Conference on Intelligent Data Science Technologies and Applications (IDSTA)10.1109/IDSTA58916.2023.10317863(76-84)Online publication date: 24-Oct-2023
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