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Debate Stance Classification Using Word Embeddings

  • Anand KonjengbamEmail author
  • Subrata Ghosh
  • Nagendra Kumar
  • Manish Singh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11031)

Abstract

Online debate sites act as a popular platform for users to express and form opinions. In this paper, we propose a novel unsupervised approach to perform stance classification of two-sided online debate posts. We propose the use of word embeddings to address the problem of identifying the preferred target of each aspect. We also use word embeddings to train a supervised classifier for selecting only target related aspects. The aspect-target preference information is used to model the stance classification task as an integer linear programming problem. The classifier gives an average aspect classification accuracy of 84% on multiple datasets. Our word embedding based stance classification approach gives 19.80% higher user stance classification accuracy (F1-score) compared to the existing methods. Our results suggest that the use of word embeddings improves accuracy and enables us to perform stance classification without the need for external domain-specific information.

Keywords

Two-sided online debate Stance classification Text mining 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Anand Konjengbam
    • 1
    Email author
  • Subrata Ghosh
    • 1
  • Nagendra Kumar
    • 1
  • Manish Singh
    • 1
  1. 1.Indian Institute of Technology HyderabadSangareddyIndia

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