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Enhancing Business Intelligence by Means of Suggestive Reviews
Joint Authors
Tahir, Muhammad
Qazi, Atika
Cambria, Erik
Syed, Karim Bux Shah
Raj, Ram Gopal
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
Appropriate 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.
American Psychological Association (APA)
Qazi, Atika& Raj, Ram Gopal& Tahir, Muhammad& Cambria, Erik& Syed, Karim Bux Shah. 2014. Enhancing Business Intelligence by Means of Suggestive Reviews. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051463
Modern Language Association (MLA)
Qazi, Atika…[et al.]. Enhancing Business Intelligence by Means of Suggestive Reviews. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1051463
American Medical Association (AMA)
Qazi, Atika& Raj, Ram Gopal& Tahir, Muhammad& Cambria, Erik& Syed, Karim Bux Shah. Enhancing Business Intelligence by Means of Suggestive Reviews. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051463
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051463