Unmasking Deception: Analyzing Fake Product Reviews through Machine and Deep Learning

Abstract

Online product evaluations have become a vital resource for shoppers looking for knowledge and direction for their purchases in the age of digital commerce. The prevalence of fraudulent product evaluations has jeopardized this priceless source of knowledge, seriously compromising the trustworthiness and integrity of online review systems. By deeply examining the identification and analysis of phony product evaluations and utilizing the capabilities of machine and deep learning algorithms, this thesis aims to address this problem. This research project starts by gathering a wide range of product reviews from different online sources. We make sure the data is consistent and of good quality by using rigorous preprocessing. We then extract important features from the reviews, like sentiment, language, and metadata, to help us analyze them. We use regular machine learning models to figure out if the reviews are real or fake, which helps us measure performance. Then, we use deep learning techniques like CNNs to get into the details of the text, which helps us detect fake reviews more accurately. This study also emphasizes the interpretability and explainability of model predictions, offering insight into the variables influencing the detection of false reviews. Our algorithms are applied to real-world datasets and settings, proving their efficacy in identifying fraudulent product evaluations across a variety of sectors, allowing us to evaluate the practical value of our research. In addition to algorithmic skill, ethical issues relating to privacy and fairness in fake review analysis are thoughtfully addressed, ensuring that the creation and application of these models are in line with responsible AI practices. To sum up, this thesis helps with continuous efforts to protect the integrity of online review systems, allowing customers to make wise decisions, and upholding the reliability of e-commerce platforms. By exploring the complex world of bogus products,