Spam Detection in Online Comments Based on Feature Weight Breakdown
Sherin MariamJohn1, K. Kartheeban2
1Sherin MariamJohn, Department of Computer Science Engineering, Kalasalingam Academy of Research and Education Krishnankoil (Tamil Nadu), India.
2K. Kartheeban, Department of Computer Science Engineering, Kalasalingam Academy of Research and Education Krishnankoil (Tamil Nadu), India.
Manuscript received on 09 December 2019 | Revised Manuscript received on 21 December 2019 | Manuscript Published on 30 December 2019 | PP: 896-900 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11401292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1140.1292S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The user reviews posted online by the Internet users about a product plays a vital role in determining its success in the market. The reviews also influence the purchase decision of the consumers. The chances of getting cheated by fake reviews are very high because detecting spams in reviews is not an easy task either manually or automatically. Hence there is a need to evolve new techniques and methods to outperform the smartness of spammers. In this paper, we propose a Heterogeneous Feature Weight Analysis framework for extracting various features related to the review and certain parameters are calculated from these features to form a pattern for deceptive reviews. The features associated with the review are review content, review rating and user centric characteristics which are pulled out from the dataset retrieved from Amazon. This analysis has helped us to categorize reviews into normal and suspicious reviews. We have executed our algorithm in Python software and were able to achieve an accuracy of 71.6% inprediction.
Keywords: Fake Reviews, Detect Spam, Sentiment Analysis, Feature Analysis, Online Reviews.
Scope of the Article: Online Learning Systems