# Spam Review Detection Techniques: A Systematic Literature Review
## 3 Transformation
### Probabilistic Classifier  
The probabilistic approach is different from other approaches in a way that certain changes between different reviews are expressed statistically rather than some rules that are written by a human or machine [54]. **Bayesian Network:**  
A Bayesian network shows the probability of the relationship among different nodes (features) [55], and the feature is an element of a review that is being used to classify the review. Moreover, each node of the graphical model represents a random variable and the edge represents the probability dependence between random variables. The relationship between different edges is represented by Directed Acyclic Graphs. The probability of a node occurring is the product of the probability that the random variable in the node occurs given that the parents have occurred. In the following equation,\(P(x_{1},\ldots\ldots x_{n})\) is the probability of any node \(x_{i}\) and \(P_{a}(x_{i})\) is the probability of the parent. Moreover, \(P(x_{i}\mid P_{a}(x_{i}))\) is also called conditional probability. \[P(x_{1},\ldots\ldots x_{n})=\prod P(x_{i}\mid P_{a}(x_{i}))\]  
This network model has been used in previous works to find spam reviews about any product or group of spam reviewers. [PERSON] et al. [27] crawled product reviews from Epinions.com and applied the Naive Bayesian algorithm. They claimed 63% accuracy in the detection of spam reviews. [PERSON] et al. [34] used Arabic opinion reviews from TripAdvisor and applied the Naive Bayesian classifier for the spam detection. They claimed 99% accuracy. Similarly, the system proposed by reference [56] reported 94% accuracy on a customer review dataset by employing the Naive Bayesian classifier. **Naive Bayes:**  
Naive Bayes (NB) classifier is also called a linear classifier and is used for both classification as well as training purposes. This is a probabilistic classifier method based on [PERSON] theorem. Moreover, Naive Bayes classifiers are based upon the naive assumptions that the features in a dataset are mutually independent. The following equation is the mathematical representation of Naive Bayes classifier. \[\mathrm{P}(\mathrm{C}|\mathrm{X})=\frac{\mathrm{P}(\mathrm{X}|\mathrm{C}) \mathrm{P}(\mathrm{C})}{P(x)}\]  
\(\mathrm{P}(\mathrm{C}|\mathrm{X})\) is the posterior probability of the target class with the given predicate attribute. \(\mathrm{P}(\mathrm{C})\) is the prior probability of class. \(\mathrm{P}(\mathrm{X}|\mathrm{C})\) is the probability of the predictor class. \(\mathrm{P}(x)\) is the prior probability of the predictor. There are different types of Naive Bayes with different uses. It was observed from the literature review that the Naive Bayes method is further divided into two text classification methods: (1) Multi-variate Bernoulli Naive Bayes is used when feature vector is represented by 0 s and 1 s, where 0 s indicate a feature that does not occur in the review and 1 s represents a feature that occurs in the review; (2) Multinomial Naive Bayes is typically used for discrete counts to determine how often a word occurs in the document. **Maximum Entropy:**  
Maximum entropy (ME) is used when there are only two outcomes of the classification. Maximum entropy model assigns a class by computing a probability from an exponential function of different features and assigns a different weight to each class [57]. Logistic Regression (LR) extracts a set of weighted features from the author's reviews, takes a log, and then each different feature is multiplied by the weight and calculated. [PERSON] et al. [35] used a crawled dataset from the Amazon website and applied Logistic Regression learner. They extracted review content and reviewer specific features and reported 78% accuracy. **iv. Linear Classifier**  
Linear classifiers utilize a linear combination of feature values of reviews and work well for the review classification problem, as it takes less time to train as compared to a non-linear classifier [58; 59].