E-Commerce Spam Review Detection using Machine Learning

E-Commerce Spam Review Detection using Machine Learning

Yogansh Wagh1, Sarfaraz Ali2, Apurva Bobade3, Aditi Bhalekar4, Prof. Rajaram Ambole5

1Department of Computer Engineering, VPKBIET, Baramati

2Department of Computer Engineering, VPKBIET, Baramati

3Department of Computer Engineering, VPKBIET, Baramati

4Department of Computer Engineering, VPKBIET, Baramati

5Department of Computer Engineering, VPKBIET, Baramati

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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.