Abstract
Any E-Commerce website gets bad reputation if they sell a product which has bad review, the user blames the e-Commerce website rather than manufacturers most of the times. In some review sites some great audits are included by the item organization individuals itself so as to make so as to deliver false positive item reviews. To eliminate these type of fake product review, we will create a system that finds out the fake reviews and eliminates all the fake reviews by using machine learning. We also remove the reviews that are flood by a marketing agency in order to boost up the ratings of a particular product. Finally Sentiment analysis is done for the genuine reviews to classify them into positive and negative. We will use Bag-of-words to label individual words according to their sentiment.
Highlights
INTRODUCTIONAs the vast majority of the general population require survey about an item before spending their cash on the item
We used Gaussian NB but the Bernoulli NB gave a better accuracy of 95.90%. In this Paper it is seen that sentiment analysis play vital role to make business decision about product/services
Major challenges in Sentiment Analysis includes feature weighting which plays a crucial role for good classification
Summary
As the vast majority of the general population require survey about an item before spending their cash on the item. Individuals go over different surveys in the site yet these audits are certified or counterfeit isn’t identified by the client. To find out fake review in the site this Product Review Monitoring and Removal and Sentimental Analysis of Genuine Reviews framework is presented. This framework will find out fake surveys made by social media optimization team by distinguishing the IP address. Address numerous multiple times it will illuminate the administrator to expel that survey from the framework. This system helps the user to find out correct review of the product
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