# Spam Review Detection Techniques: A Systematic Literature Review
## 4 Conclusions  
This study presented a systematic literature review of the spam review detection domain and highlighted recent research contributions in the form of different feature engineering approaches, spam review detection methods, and different measures used for performance evaluation. To extract precise pragmatic evidence, this work planned review methodology, focused on the search string, raised research questions, selected papers from renowned publishers, and used formal inclusion and exclusion study assessment criteria. A total of 1690 papers published from January 2007 to December 2018 were selected based on the search string, and after applying a title-based search 165 papers were shortlisted. Finally, by applying an abstract-based search, full-length analysis, and snow ball tracking, 76 publications were finalized for further study. Moreover, quality assessment criteria to determine the relevance and validity of the research domain appropriate to the selected publications were utilized. Table 5 presented a summary of the selected publications in the form of publisher and proceedings. A principle advantage of this study is that to the best of our knowledge, this is the first attempt to compile all the existing studies of spam review detection techniques using the SLR approach. Furthermore, the output of this study can be useful for further research in the area of spam review detection. Main findings of this systematic literature review are summarized below:  
**Feature engineering techniques:** Feature engineering can have a considerable influence on the performance of spam review detection methods [85; 86; 87]. Recent works on spam review detection used the same dataset, method, and evaluation measures, but reported varying results after applying different feature engineering approaches [4; 15; 32; 33]. Table 11 presents the accuracy of works that used different feature approaches. It is also observed that the individual linguistic features and combination of linguistic and behavioral features yield better accuracy. **Spam review detection methods:** Review of existing works showed that there are mainly two spam review detection techniques, namely machine learning and Lexicon-based. However, this domain is relatively new, therefore only a few studies on spam review detection have been conducted to date [88]. It is found that most of the existing studies focused on supervised machine learning approaches. Supervised learning must have a labeled dataset. Real-world datasets are difficult to acquire in this domain and most of the available datasets are synthetically created. However, building models based on synthetically created datasets are not very reliable. For example, when an artificial AMT dataset [15; 31; 32] and Yelp's filtered real review dataset were used by the same framework, it was observed that Yelp's real-world dataset produced low accuracy, especially with n-gram linguistic features [4]. Lexicon approach is based on a set of precompiled sentimental terms, idioms, and phrases. Previous works have primarily used two subclasses of the Lexicon approach: Dictionary and Corpus-based. The Dictionary-based approach is used to collect a preliminary set of terms that are typically gathered in a manual way, whereas the Corpus-based approach is used to providedictionaries linked to a particular area, and therefore produce better accuracy as compared to the Dictionary-based approaches. **Evaluation measures:** It is observed that earlier works in the area of spam review detection have been evaluated using precision, recall, f-measure, and area under ROC curve [59] evaluation measures. Supervised machine learning methods mostly used AUC evaluation measures. Since most works are based on synthetically created review datasets, there is a need to evaluate their performance more rigorously with respect to how these methods will perform on real-world data.