Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels

  • Mohamad Hardyman Barawi
  • Chenghua Lin
  • Advaith Siddharthan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10260)


In this paper, we propose a simple yet effective approach for automatically labelling sentiment-bearing topics with descriptive sentence labels. Specifically, our approach consists of two components: (i) a mechanism which can automatically learn the relevance to sentiment-bearing topics of the underlying sentences in a corpus; and (ii) a sentence ranking algorithm for label selection that jointly considers topic-sentence relevance as well as aspect and sentiment co-coverage. To our knowledge, we are the first to study the problem of labelling sentiment-bearing topics. Our experimental results show that our approach outperforms four strong baselines and demonstrates the effectiveness of our sentence labels in facilitating topic understanding and interpretation.



This work is supported by the awards made by the UK Engineering and Physical Sciences Research Council (Grant number: EP/P005810/1, EP/P011829/1).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohamad Hardyman Barawi
    • 1
  • Chenghua Lin
    • 1
  • Advaith Siddharthan
    • 1
  1. 1.Computing ScienceUniversity of AberdeenAberdeenUK

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