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  Review centric review spam detection is the most common form of review spam detection, which uses machine learning techniques to build models using the content and metadata of the reviews. Supervised learning refers to the task of learning from labeled data and is the most prevalent method used for review spam detection in the literature. Unfortunately, this method requires labeled data in order to train a classifier, presenting the challenge of needing methods to procure and accurately label a sufficient amount of data, which can be problematic in the field of review spam detection. Conversely, unsupervised learning uses unlabeled data to find unseen relationships between instances independent of a class attribute. An example of unsupervised learning is clustering, which is able to group instances of unlabeled data based upon some type of similarity function. Semi-supervised learning is a combination of the two and uses a few labeled instances in combination with a large number of unlabeled instances to train a classifier and has shown promise in the area of review spam detection. These methods are summarized in Table 8 and the following subsections outline research conducted using these different types of learning in the domain of review spam detection. Shojaee et al. [11] proposed a novel method for detecting review spam by using Stylometric (Lexical and Syntactic) features. (For further details on Stylometric features see Abbasi et al. [31]). The features in this work are categorized as either lexical features or syntactic features. Lexical features are character/word based features, while syntactic features represent the writing style of the reviewers at the sentence level, such as occurrences of punctuations or function words. In this work they built SVM and Naïve Bayes classifiers on the dataset created by Ott et al. [3] using a hybrid set of both the lexical and syntactic features and compared this with using either lexical or syntactic features alone. Using 10-fold cross validation, they observed that the hybrid feature set using the SVM learner achieved the highest performance, an F-measure of 84 %. Additionally, SVM outperformed Naïve Bayes for all sets of features. A potential concern of this study is that the model was trained and evaluated on synthetic fake reviews. Due to this, it is possible that the classifier performance measured is a poor indication of real world performance, as was demonstrated by Mukherjee et al. [32]. Also there is no comparison evaluation to determine if using these Stylometric features in addition to n-gram features enhances classification performance.

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