ISSN: 0973-7510

E-ISSN: 2581-690X

T. Sumathi1, S. Karthik2 and M. Marikkannan3
1Department of Computer Science and Engineering, Institute of Road and Transport Technology, Erode,Tamilnadu, India.
2Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India.
3Department of Computer Science and Engineering, Instutute of Road and Transport Technology, Erode, Tamilnadu, India.
J Pure Appl Microbiol. 2015;9(Spl. Edn. Aug.):17-23
© The Author(s). 2015
Received: 19/02/2015 | Accepted: 22/04/2015 | Published: 31/08/2015

Opinion mining, a sub-discipline of information retrieval and concerns not with what a document is about, but with its expressed opinion. Feature selection is an important step in opinion mining, as customers express product opinions separately according to individual features. Statistical techniques like Correlation based Feature Selection (CFS) have been extensively used for feature selection to reduce the corpus size. Feature selection is NP hard and selecting features using statistical techniques is suboptimal. In this work a novel feature selection technique using Multi Objective Artificial Bee Colony algorithm is proposed. The proposed technique is evaluated using Fuzzy Unordered Rule Induction Algorithm (FURIA) classifier.  Results show improved classification accuracy in the proposed technique.


Opinion Mining, Fuzzy Unordered Rule Induction Algorithm (FURIA), Correlation based Feature Selection (CFS), Artificial Bee Colony (ABC), Feature selection, Inverse Document Frequency (IDF)

Article Metrics

Article View: 887

Share This Article

© The Author(s) 2015. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.