Automatically Labelling Sentiment-bearing Topics with Descriptive Sentence Labels

Mohamad Hardyman Bin Barawi, Chenghua Lin, Advaith Siddharthan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.
Original languageEnglish
Title of host publicationThe 22nd International Conference on Natural Language & Information Systems (NLDB)
Place of PublicationBelgium
PublisherSpringer
Pages299-312
Number of pages14
Volume10260 LNCS
ISBN (Print)9783319595689
DOIs
Publication statusPublished - 2017
Event22nd International Conference on Natural Language & Information Systems - Liège, Belgium
Duration: 21 Jun 201723 Jun 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743

Conference

Conference22nd International Conference on Natural Language & Information Systems
Abbreviated titleNLDB 2017
CountryBelgium
CityLiège
Period21/06/1723/06/17

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  • Cite this

    Barawi, M. H. B., Lin, C., & Siddharthan, A. (2017). Automatically Labelling Sentiment-bearing Topics with Descriptive Sentence Labels. In The 22nd International Conference on Natural Language & Information Systems (NLDB) (Vol. 10260 LNCS, pp. 299-312). (Lecture Notes in Computer Science). Springer . https://doi.org/10.1007/978-3-319-59569-6_38