Discovering contextual tags from product review using semantic relatedness

Lim, Soon Chong Johnson, Wang, Shilong and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2014. Discovering contextual tags from product review using semantic relatedness. Journal of Industrial and Production Engineering 31 (2) , pp. 108-118. 10.1080/21681015.2014.895966

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Abstract

In the design community, while a number of studies that have focused on studying product reviews in various design analysis perspectives, contextual annotation of identified terms (e.g. product features) has not been fully explored. This paper proposed a learnable approach towards discovering contextual tags from product reviews. A ranking algorithm, FacetRank, is proposed to rank important key terms along with an approach to discover contextual annotation of the terms from review documents. The evaluation of our proposal is performed using two annotated corpus to examine our algorithm’s contextual tagging performance. A case study using a small collection of laptop reviews is also reported to showcase how our algorithm can be applied towards product feature understanding and multi-faceted product ontology development. Finally, we conclude this paper with some indications for future work.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	Taylor and Francis
ISSN:	2168-1015
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	3 February 2014
Last Modified:	08 Nov 2023 05:17
URI:	https://orca.cardiff.ac.uk/id/eprint/68107

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