Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels

Barawi, Mohamad Hardyman; Lin, Chenghua and Siddharthan, Advaith (2017). Automatically Labelling Sentiment-Bearing Topics with Descriptive Sentence Labels. In: The 22nd International Conference on Natural Language & Information Systems (NLDB), Springer, Belgium, pp. 299–312.

DOI: https://doi.org/10.1007/978-3-319-59569-6_38

Abstract

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.

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About

• Item ORO ID
• 51052
• Item Type
• Conference or Workshop Item
• ISBN
• 3-319-59568-7, 978-3-319-59568-9
• ISSN
• 0302-9743
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Not Set	EP/P005810/1	UK Engineering and Physical Sciences Research Council
  Not Set	EP/P011829/1	UK Engineering and Physical Sciences Research Council

• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2017 Springer International Publishing AG
• Depositing User
• Advaith Siddharthan