Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/104763
Title: Enhancing business intelligence by means of suggestive reviews
Authors: Qazi, Atika
Raj, Ram Gopal
Tahir, Muhammad
Cambria, Erik
Syed, Karim Bux Shah
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2014
Source: Qazi, 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-.
Series/Report no.: The scientific world journal
Abstract: 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.
URI: https://hdl.handle.net/10356/104763
http://hdl.handle.net/10220/20292
ISSN: 2356-6140
DOI: 10.1155/2014/879323
Rights: Copyright © 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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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