Abstract
With the proliferation of e-commerce platforms, the authenticity of online reviews has become increasingly crucial. In this research, we present a method for detecting spam reviews in e-commerce platforms, employing the Random Forest algorithm. Leveraging a dataset comprising reviews from Amazon Yelp dataset, we employ a combination of Natural Language Processing (NLP) techniques and text processing methods to uncover underlying patterns distinguishing between genuine and fake reviews. Real-time data scraping from the Amazon website facilitated the acquisition of a diverse range of reviews, subsequently stored in a CSV file for analysis. These reviews stored in CSV file is fed to model for prediction. Our model effectively discerns between authentic and spam reviews, offering a valuable tool for maintaining the integrity of e-commerce platforms and ensuring informed consumer decision-making. Key Words: Web Scrapping, Text Mining, Sentiment Analysis.
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