An Empirical Analysis of Different Techniques for Spam Detection

Abstract

Digital shopping has been increasingly popular over the years, as a result of the increased demand for internet access. This is due to the fact that a large number of items and services are readily available online. Thus, customer and organisational feedback on many of these goods & services is extremely valuable to both parties involved. Unfortunately, in order to generate profit or marketing, scammers frequently fabricate bogus reviews. Customers and organisations are unable to form an informed perspective about the merchandise because of the phoney reviews produced by scam artists. As a result, it is necessary to recognise and eradicate bogus reviews or spam reviews in order to avoid misleading potential customers in the future. Spam has emerged as a significant concern on the Internet and in electronic communication in recent years. There have been numerous strategies suggested to combat them. This study presents a summary of the current approach of revising spam filtering, as well as its advantages and disadvantages. The classification of traditional and learning methods, as well as their evaluation and correlation, are all included in this section. Spam articles are tested on some personal enemies and compared to each other. The planning application spam detection technique has been taken into consideration.