Abstract
Abstract: As the trend to shop online is growing day by day and lot of people are interested in purchasing the products of their need from the online stores. This way of shopping does not take a lot of time of a customer. In this case reviews on online websites play a important role in sales of the product because people try to get all the pros and cons of any product before they buy it. Most of the people needs genuine information about the product while online shopping. Before spending their money on particular product can analyse the various comments in the website. In this scenario, they did not recognize whether it may be fake or genuine. Customer place the order for particular product only by considering the reviews of that product. Here, it might be possible that reviews are fake. Now here query is which are fake reviews? Fake reviews may be good or bad compliment on the products. To detect such type of reviews we have developed the system. In this research, the dataset of different fake reviews provided by Flipkart are considered where reviews sentiments are included and using the LOGISTIC REGRESSION CLASSIFIER the reviews are classified into two categories i.e. fake and genuine. So user can save his/her time only by reading genuine reviews and gives accuracy about the product. Keywords: Fake reviews, review sentiment, logistic regression classifier, detection, feature extraction, web scrapping.
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