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A fuzzy decision support method for customer preferences analysis based on Choquet integral

Vu, Huy Quan, Li, Gang and Beliakov, Gleb 2012, A fuzzy decision support method for customer preferences analysis based on Choquet integral, in FUZZ-IEEE 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems, IEEE Computer Society, Los Alamitos, Calif., pp. 1-8.

Document type: Conference Paper
Collection: School of Information Technology
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Title A fuzzy decision support method for customer preferences analysis based on Choquet integral
Author(s) Vu, Huy Quan
Li, Gang
Beliakov, Gleb
Conference name International Conference on Fuzzy Systems (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 10-15 Jun. 2012
Title of proceedings FUZZ-IEEE 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems
Editor(s) [unknown]
Publication date 2012
Conference series International Conference on Fuzzy Systems
Start page 1
End page 8
Total pages 8
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) continents
data mining
decision making
feature extraction
indexes
industries
Summary The explosion of the Web 2:0 platforms, with massive volume of user generated data, has presented many new opportunities as well as challenges for organizations in understanding consumer's behavior to support for business planning process. Feature based sentiment mining has been an emerging area in providing tools for automated opinion discovery and summarization to help business managers with achieving such goals. However, the current feature based sentiment mining systems were only able to provide some forms of sentiments summary with respect to product features, but impossible to provide insight into the decision making process of consumers. In this paper, we will present a relatively new decision support method based on Choquet Integral aggregation function, Shapley value and Interaction Index which is able to address such requirements of business managers. Using a study case of Hotel industry, we will demonstrate how this technique can be applied to effectively model the user's preference of (hotel) features. The presented method has potential to extend the practical capability of sentiment mining area, while, research findings and analysis are useful in helping business managers to define new target customers and to plan more effective marketing strategies.
ISBN 9781467315050
9781467315074
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048283
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