Rupesh Kumar Dewang
ABSTRACT
In the era of e-commerce, customers are trust on on-line review. Reviews help them to make right decisions to buy a product or hire a service. Spammer are writes the fake review to promote or demote the target products. Reviews are spam or non-spam; this is the biggest problem for E-commerce business. Moreover, the spam detection problem is complex task. Spammers are inventing new methods to writing spam reviews. It cannot be recognized easily. In this paper, we have extracted some new writing style features like, attractive text ratio and function word ratio and corleone-based features like, lexical validity and text like fraction and compared with already existing features. We have applied support vector machine (SVM), logistic regression, random forest, Jrip, functional tree, Naive Bayes, J48, PART algorithms for classification of review as a spam or non-spam. The SVM, random forest and logistic regression gives the 68% accuracy.
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