Semi-supervised Learning with Ensemble Method for Online Deceptive Review Detection

Authors(2) :-Miss. Priyanka Shinde, Prof. Hemlata Channe

Now-a-days not only organizers but users also prefer to give opinion after using any kind of resource. Opinion of user is very important for business. Because of opinion of actual user further consumers should think to use that resource. In Business, opinion review has great impact to economical bottom line. Unsurprisingly, opportunistic individuals or groups have attempted to abuse or manipulate online opinion reviews (e.g., spam reviews) so that they credit or degrade the target product. Because of this detecting deceptive and fake opinion reviews is a topic of ongoing research interest. In this paper semi-supervised learning approach with ensemble learning methods is used for finding out these spam reviews. Utility is demonstrated using a data set of online hotel booking websites.

Authors and Affiliations

Miss. Priyanka Shinde
Pune Institute of Computer Technology, Pune, Maharashtra, India
Prof. Hemlata Channe
Pune Institute of Computer Technology, Pune, Maharashtra, India

Opinion Spam, Multilabel and Multiclass, Ensemble of classifiers , Co-training, PU learning, EM algorithm

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Publication Details

Published in : Volume 3 | Issue 6 | July-August 2018
Date of Publication : 2018-07-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 415-422
Manuscript Number : CSEIT183627
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Miss. Priyanka Shinde, Prof. Hemlata Channe, "Semi-supervised Learning with Ensemble Method for Online Deceptive Review Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 6, pp.415-422, July-August-2018.
Journal URL : http://ijsrcseit.com/CSEIT183627

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