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Barawi, Mohamad Hardyman; Lin, Chenghua and Siddharthan, Advaith
(2017).
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