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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 (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-59569-6_38
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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.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 Springer International Publishing AG
ISBN: 3-319-59568-7, 978-3-319-59568-9
ISSN: 0302-9743
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetEP/P005810/1UK Engineering and Physical Sciences Research Council
Not SetEP/P011829/1UK Engineering and Physical Sciences Research Council
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 51052
Depositing User: Advaith Siddharthan
Date Deposited: 21 Sep 2017 10:26
Last Modified: 21 Sep 2017 10:26
URI: http://oro.open.ac.uk/id/eprint/51052
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