Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104763
Full metadata record
DC FieldValueLanguage
dc.contributor.authorQazi, Atikaen
dc.contributor.authorRaj, Ram Gopalen
dc.contributor.authorTahir, Muhammaden
dc.contributor.authorCambria, Eriken
dc.contributor.authorSyed, Karim Bux Shahen
dc.date.accessioned2014-08-15T04:27:27Zen
dc.date.accessioned2019-12-06T21:39:11Z-
dc.date.available2014-08-15T04:27:27Zen
dc.date.available2019-12-06T21:39:11Z-
dc.date.copyright2014en
dc.date.issued2014en
dc.identifier.citationQazi, A., Raj, R. G., Tahir, M., Cambria, E., & Syed, K. B. S. (2014). Enhancing Business Intelligence by Means of Suggestive Reviews. The Scientific World Journal, 2014, 879323-.en
dc.identifier.issn2356-6140en
dc.identifier.urihttps://hdl.handle.net/10356/104763-
dc.description.abstractAppropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons.en
dc.language.isoenen
dc.relation.ispartofseriesThe scientific world journalen
dc.rightsCopyright © 2014 Atika Qazi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.subjectDRNTU::Engineering::Computer science and engineeringen
dc.titleEnhancing business intelligence by means of suggestive reviewsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1155/2014/879323en
dc.description.versionPublished versionen
dc.identifier.pmid25054188-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:SCSE Journal Articles
Files in This Item:
File Description SizeFormat 
879323.pdf1.73 MBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 20

25
Updated on Jun 20, 2024

Web of ScienceTM
Citations 20

9
Updated on Oct 29, 2023

Page view(s) 20

672
Updated on Jun 24, 2024

Download(s) 20

264
Updated on Jun 24, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.