A comprehensive survey of various methods in opinion spam detection

With the development of technology, e-marketing and business competition, reviews significantly affect people's lives. Consumers of e-commerce sites share their experiences, referencing other user reviews when making consumption decisions and evaluating product quality. People's dependence on review information and business competition has triggered the emergence of false reviews. False reviews refer to the promotion or demotion of the quality of the product. False reviews can easily confuse new users' opinions or judgments, due to which both the consumer and the company are affected. Addressing this issue, spam review is a new research problem today, and even humans do not quickly classify spam reviews with high accuracy. Machine learning methods and natural language processing techniques effectively recognise false reviews and help users get benign review information. In this research, we have categorized and classified spam review detection methods in the direction of review features, reviewer features, and the spammer's group features. In this article, we focus on three approaches for detecting false reviews: spam review detection, spammer detection, and spammer-group detection. This article provides a motivational analysis of the three types of research and, in particular, compares them to aspects of feature design, model methods, datasets, and rating indicators.