Abstract: Most online stores allow their consumers to post reviews of their products and services. These reviews' presence can be used as a source of knowledge. Reviews are becoming a more important source of information for consumers. Unfortunately, phony reviews by certain parties that attempted to produce fake reviews in order to boost the popularity of their product or to disparage the competitor's goods have undermined the significance of the review. The goal of this paper is to identify fake reviews on e-commerce sites using the text, rating properties, and other information from a review. The project also proposes to classify the reviews as positive or negative based on the text used in the reviews, ratings given to the product so on.

Keywords: Supervised Learning, Flask, Framework, Web Application, Naïve Bayes

PDF | DOI: 10.17148/IJARCCE.2022.11733

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