## 4 Literature Analysis, Discussion and Future Directions  
In this section, detailed analyses and interpretations of the presented works are proposed. These studies have their advantages and disadvantages. Therefore in this discussion, we will investigate their definition, strengths style, and weakness. The first genre of the studies focuses on works that depend on behavior features. These type of features considers important and essential when trying to identify spam reviews. Due to the fact that they rely on the attitude of the spammers themselves. As such, many researchers combined this method with their main approach thanks to its ability to detect some kind of reviews. Examples of such behaviors could be the style of writing, the time of posting, rating of each review, name of their users, and so on. However, the behavior features technique could be tricked by using the correct methods (for example, changing the style of writing) without considering other combined schemes with this technique, such as machine learning methods. As for the second genre, the supervised learning detection model is a well-known technique applied not only for spam reviews but also in different security and other domains. Supervised learning has a powerful ability to recognize the hidden pattern of reviews, including the relation of reviews, reviewers, products, features, and labels between each other. Such a pattern can help improve the identification of spam reviews efficiently. Hence, it is considered one of the best methods to detect spam reviews recently in the literature. Nevertheless, this technique has an essential weakness when performing. The lacking of many labeled data and the preparation of the data. This is happened due to the fact of labeling and pre-processing of the data consume time, effort, and money. Figure 7: The most keywords appearance form the reviewed studies. Further, the semi-supervised learning method attempted to solve what supervised learning lacked. Handling unlabeled data can be resolved to utilize the traits of semi-supervised learning without any problem. Semi-supervised can perform with a few numbers of labeled instances while the rest of the instances are unlabeled. That's why semi-supervised learning consider relatively inexpensive, where no need to consume money and time on labeling the whole dataset. In other words, it has the traits of both the supervised and unsupervised learning methods. On the other hand, the semi-supervised technique has two main disadvantages, which are the instability of iteration results and usually obtaining low accuracy. Deep Learning is considered the future of machine learning, where it can detect spam reviews efficiently and dynamically. Particularly in spam reviews, where the construction of features is a necessity. Deep learning can handle different types of features, applications, and datasets. Thus, can easily outperform other approaches in spam reviews detection. However, deep learning requires huge data (big data) to perform and outperforms other methods. Also, it consumes a lot of time to train the model. Therefore, it needs more effort to deal with when optimal performance is the objective. Moreover, linguistics approaches usually depend on the language characteristics and the context used in the reviews. Also, it addresses other languages than English alongside various regions to detect spam reviews. These languages and regions have different characteristics. Hence, it is important to have such a detection approach that can handle the linguistics, context, culture, regions, and multilingual spam reviews. But this method is hard to perform without the proper persons that can understand the used language. Also, it lacks the diversity of linguistic spam reviews detection methods. The ensemble machine learning-based achieved excellent performance for spam reviews detection. Either using the voting method or the stacking method (a new classifier learn from the previous one).