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    This paper is organised as follows: Section 2 describes the existing feature extraction techniques. Section 3 describes detailed descriptions of the publicly available datasets with descriptions in addition to a summary Table. Section 4 presents the existing methods for fake review detection and the limitations for each technique, including traditional classical machine learning and neural network models. Section 5 presents the experiments of promising approaches for fake review detection. Section 6 presents the current gaps in this research area and the possible future direction. Section 7 presents the conclusion. Fig. 1 shows the outline of this survey. Cross domain fake review detection. The cross-domain problem needs to be effectively addressed. The issue of lack of annotation datasets is disappointing in fake review detection. Applying a model trained in the source domain and tested in the target domains is a significant research direction. The existing literature focused only on one domain of fake review detection, so these proposed models failed when training in the domain and tested in other domains. For example, the authors [80] trained the model in the domain and tested the other domain. The experimental results show the performance has significantly dropped compared with the performance in the same domain. More research and investigation are needed for cross-domain fake reviews detection [67], [186].

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