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Emoji-Based Co-Attention Network for Microblog Sentiment Analysis

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Part of the book series:Communications in Computer and Information Science ((CCIS,volume 1516))

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Abstract

Emojis are widely used in online social networks to express emotions, attitudes, and opinions. As emotional-oriented characters, emojis can be modeled as important features of emotions towards the recipient or subject for sentiment analysis. However, existing methods mainly take emojis as heuristic information that fails to resolve the problem of ambiguity noise. Recent researches have utilized emojis as an independent input to classify text sentiment but they ignore the emotional impact of the interaction between text and emojis. It results that the emotional semantics of emojis cannot be fully explored. In this paper, we propose an emoji-based co-attention network that learns the mutual emotional semantics between text and emojis on microblogs. Our model adopts the co-attention mechanism based on bidirectional long short-term memory incorporating the text and emojis, and integrates a squeeze-and-excitation block in a convolutional neural network classifier to increase its sensitivity to emotional semantic features. Experimental results show that the proposed method can significantly outperform several baselines for sentiment analysis on short texts of social media.

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Acknowledgement

The research is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDC02060400.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Xiaowei Yuan, Jingyuan Hu, Xiaodan Zhang, Honglei Lv & Hao Liu

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Xiaowei Yuan & Hao Liu

Authors
  1. Xiaowei Yuan

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  2. Jingyuan Hu

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  3. Xiaodan Zhang

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  4. Honglei Lv

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  5. Hao Liu

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Corresponding author

Correspondence toXiaodan Zhang.

Editor information

Editors and Affiliations

  1. Sampoerna University, Jakarta, Indonesia

    Teddy Mantoro

  2. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  3. Sampoerna University, Jakarta, Indonesia

    Media Anugerah Ayu

  4. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  5. Universitas Indonesia, Depok, Indonesia

    Achmad Nizar Hidayanto

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Cite this paper

Yuan, X., Hu, J., Zhang, X., Lv, H., Liu, H. (2021). Emoji-Based Co-Attention Network for Microblog Sentiment Analysis. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_1

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