Detection of Fraud Reviews for a ProductJournal: International Journal for Scientific Research and Development | IJSRD (Vol.3, No. 10)
Publication Date: 2016-01-01
Authors : Janhavi Sankpal; Pooja Sankpal; Kanchan Haral;
Page : 408-410
Keywords : Primal Text Mining and Pre-processing; NLP; Mapping Text Data;
Most of us use e-shopping (Any product) these days and refer its rating or reviews before we download or buy that product. Amazon/Play store provide a great number of products but unfortunately few of those product reviews are fraud. Hence such products must be marked, so that they will be recognizable for rest of the users. Here we are comparing reviews from two sites so that we can get more clear idea. We can get higher probability of getting real reviews if we take data from multiple sites. We are proposing a system to develop an android application that will take reviews from two different websites for single product, and analyze them with NLP for positive or negative rating. In this, user will give two different URLs of two different sites for same product to the system as input. For every URL reviews and comments will be fetched separately and analyzed with NLP for positive negative rating. Then their rating will be combined together with average to give final rating for the product. As we are handling the big data here, we are using Hadoops map reduce. So it will be easier to decide which product reviews are fraud or not.
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Last modified: 2016-01-07 18:33:24