Product Reviews Classifications-A Data Science Approach

Abstract

Reviewing the product is an important step for e-commerce platforms. Getting reviews from customers and analysis of reviews consumes many resources. As the number of reviews received day by day is increasing very rapidly, reviews should be classified as fake review and Genuine reviews. The total accumulation of reviews and analysis is different from natural language processing problem. Spammers are hired for biased reviewing of products. In this paper we put a novel comparison between purchase list and reviews. We have applied a method for finding duplicate reviews; measure the total numbers of reviews and their mismatch in counts, at the end count dispersion for every product and classification of reviews. We applied data science approach for classification and visualization to get fake reviews. We label the reviews either positive or negative based on comparison between them. A data science approach is applied because for a well-known product the reviews can goes up to many thousands.