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Emoji-Based Sentiment Analysis Using Attention Networks

Published:01 June 2020Publication History
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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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    • 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
      Issue’s Table of Contents

      Copyright © 2020 ACM

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 June 2020
      • Online AM: 7 May 2020
      • Accepted: 1 March 2020
      • Revised: 1 December 2019
      • Received: 1 February 2019
      Published in tallip Volume 19, Issue 5

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