# Spam Review Detection Techniques: A Systematic Literature Review
## Discussion  
This section reviewed existing literature on spam review detection methods published between 2007 and 2018. An attempt has been made to provide researchers with a comparative analysis of different spam review detection methods and their reported accuracy. Generally, spam review detection techniques are classified into two categories. The first one is machine-learning-based methods, which are further classified into two categories, supervised and unsupervised learning. The accuracy of different supervised-learning-based works is presented in Table 12. It shows that Support Vector Machine and Naive Bayes perform better as compared to other supervised learning methods. Table 13 shows that Aspect based and K-nearest neighbors approaches perform better in unsupervised learning approaches. The second approach is Lexicon-based, which is further divided into two categories, Dictionary-based and Corpus-based methods. The Dictionary-based approach is more efficient in terms of processing time as compared to supervised learning but yields less accuracy. The Corpus-based technique depends upon the dictionary related to the specific seed words of the domain. Table 14 shows that Corpus-based and Dictionary-based approach produce better accuracy as compared to other Lexicon-based techniques. It was observed in existing literature that all spam review detection methods are effective in identifying spam reviews; however, machine-learning-based supervised approaches generally yield better results. In recent years, new network-based filtering algorithms have been proposed, which filter out good or bad opinions from review datasets to aid the potential user, and these proposed algorithms produce better accuracy as compared to existing network-based spam review detection methods [77; 78]. Assessment of RQ3: Which Performance Metrics Are Used to Evaluate the Performance of Spam Review Detection Methods? This section presents different evaluation measures that are used in the domain of spam review detection. Review of primary studies shows that evaluation measures to assess the accuracy of the \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Paper ID** & **Dataset** & **Technique** & **Accuracy** \\ \hline
[74] & IMDB, Skytrax, Tripadvisor & Group based technique & 86.6\% \\
[75] & Luce, Yoka & Group based technique & 82.6\% \\
[72] & Amazon Mechanical Turk (AMT) & Dictionary-based method & 89\% \\
[71] & Blog06 & Dictionary-based method & 78\% \\
[76] & CNET, IMDB & Machine learning (ML)+ Lexicon & 82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparison of different Lexicon-based techniques. spam review detection methods are precision, recall, and f-measure [75]. Precision is used to measure the percentage of correct instances among the known positive instances, whereas recall is used to calculate the percentage of correct instances that can be known among the entire positive instance. F-measure is a geometric mean of precision and recall. Accuracy is calculated by the following formula:  
\[\text{accuracy}=\frac{\text{\# hits}}{\text{\# total\_review}}\]  
where \(\text{\# total\_review}\) represents the number of reviews used for the experiment and \(\text{\# hits}\) represents the total positive or negative reviews that were correctly classified by the spam review detection method. Precision value can be calculated for positive or negative reviews that have been correctly classified. Therefore, the following equation is used to calculate positive precision:  
\[\text{Positive\_precision}=\frac{\text{\# positive\_well\_ classified}}{\text{\# positive\_well\_classified}+\text{\# positive\_bad\_classified}}\]  
where \(\text{\# positive\_bad\_classified}\) shows a number of negative reviews that were incorrectly classified as positive, and \(\text{\# positive\_well\_classified}\) represents original positive reviews submitted to the proposed spam review detection method. The same procedure is used for negative or neutral reviews.