ABSTRACT
With cyberspace's continuous evolution, online reviews play a crucial role in determining business success in various sectors, ranging from restaurants and hotels to e-commerce applications. Typically, a favorable review for a specific product draws in more consumers and results in a significant boost in sales. Unfortunately, a few businesses are using deceptive methods to improve their online reputation by using fake reviews of competitors. As a result, detecting fake reviews has become a difficult and ever-changing research field. Verbal characteristics extracted from review text, as well as nonverbal features such as the reviewer's engagement metrics, the IP address of the device, and so on, play an important role in detecting fake reviews. This article examines and compares various machine learning techniques for detecting deceptive reviews on various online platforms such as e-commerce websites such as Amazon and online review websites such as Yelp, among others. © 2023 IEEE.
ABSTRACT
The behavior of shopping has shifted into online shopping. Especially after Coronavirus Disease of 2019 (COVID-19), people choose online shopping rather than going to the market for economic and hygienic reasons. Reviews help the seller to make customers trust their products, but since some sellers are not honest, they use fake reviews to help boost their products. Fake reviews are commonly generated randomly by a computer bot or someone not using the product. Some researchers are already working on fake review detection to help this problem using many methods. In this paper, we compared three supervised machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF). By preprocessing the data and using the Term Frequency-Inverse Document Frequency (TF-IDF) feature, we begin the experiment process without tuning. We apply the tuning parameters to each algorithm for the other experiments using 5-fold cross-validation. The result showed that SVM algorithms outperform the best algorithms of the three before and after tuning, with 88.89% and 89.77%, respectively. © 2022 IEEE.