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
Google Scholar:	Look up in Google Scholar

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

2017 Springer International Publishing AG

ISBN:

3-319-59568-7, 978-3-319-59568-9

ISSN:

0302-9743

Project Funding Details: