Abstract:
Fake Rating Identification is a critical issue in today’s online world, as it can significantly impact consumer trust and decision-making as they can mislead customers, harm the reputation of a company, and impact the overall quality of products and services. With the proliferation of review websites and platforms, it has become increasingly easy for individuals or organizations to manipulate ratings and reviews for their own gain. In this study, we aimed to identify fake ratings and reviews using machine learning algorithms and natural language processing techniques. In this research, we aim to identify fake ratings by analyzing various factors such as the reviewer’s history, the timeliness of the review, and the language used. Our approach involves analyzing the text of the reviews, as well as the patterns of review activity over time. We evaluate the effectiveness of our method using a data set of real-world reviews and show that it is able to accurately identify fake ratings with a high degree of accuracy. Our goal is to develop a model that can accurately detect fake ratings and improve the reliability of online reviews for consumers. The results of this research will provide valuable insights for businesses and consumers looking to make informed decisions based on accurate ratings and reviews. Through our research, we hope to provide valuable insights and recommendations for online platforms, businesses, and consumers to protect against fake ratings and maintain trust in online reviews
