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Classifying facts and opinions in Twitter messages: a deep learning-based approach

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  • Swayambhu Chatterjee
  • Shuyuan Deng
  • Jun Liu
  • Ronghua Shan
  • Wu Jiao

Abstract

Massive social media data present businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts and opinions. Distinguishing facts and opinionss may significantly improve subsequent analytics tasks. In this study, we propose a deep learning-based algorithm that automatically separates facts from opinions in Twitter messages. The algorithm outperformed multiple popular baselines in an experiment we conducted. We further applied the proposed algorithm to track customer complaints and found that it indeed benefits subsequent analytics applications.

Suggested Citation

  • Swayambhu Chatterjee & Shuyuan Deng & Jun Liu & Ronghua Shan & Wu Jiao, 2018. "Classifying facts and opinions in Twitter messages: a deep learning-based approach," Journal of Business Analytics, Taylor & Francis Journals, vol. 1(1), pages 29-39, January.
    • Handle: RePEc:taf:tjbaxx:v:1:y:2018:i:1:p:29-39
    DOI: 10.1080/2573234X.2018.1506687
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    Cited by:

    1. Marcio Salles Melo Lima & Enes Eryarsoy & Dursun Delen, 2021. "Predicting and Explaining Pig Iron Production on Charcoal Blast Furnaces: A Machine Learning Approach," Interfaces, INFORMS, vol. 51(3), pages 213-235, May.

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