With the development of the Internet, users tend to refer to online reviews before shopping, travelling, and dining. After that, they write reviews to express their own opinions. Online reviews are increasingly of great value. The significant guiding role of reviews playing in consumers' decisions has given rise to false comments, which we call review spam. The review spam refers to the comments written by users that do not meet the true characteristics of products, due to factors such as commercial profits and personal bias. Spammers imitate the writing style of true reviewers so that customers can hardly discriminate the review spam. Scholars at home and abroad use natural language processing techniques to detect review spam. From the perspective of feature engineering, review spam detection methods are divided into three types:the linguistic and behavior based, the graph based, and the representation learning based. This survey mainly describes the general process of review spam detection, summarizes feature designing of the models, and makes a comparison among three types of methods. Furthermore, the most commonly used datasets are introduced. Finally, it explores the research directions in the future.