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  2) traditional statistical unsupervised learning in detecting fake rreviews positive and unlabelled learning method has been extensively utilized in text classification and achieved good results [145], [146]. typically, yafeng et al. [9] proposed a novel positive and unlabelled learning method (pu), called mixing population, and individual nature pu learning method to detect fake reviews. some reliable negative examples were identified from the unlabelled dataset. the integration of latent dirichlet and k means provided some representative positive examples and negative examples. all fake reviews were clustered into distinct groups based on the dirichlet process mixture model. then, they mixed two schemes - individual nature and population nature to identify the group label of fake reviews. the final classifier was built using multiple kernel learning. the experimental results showed that the proposed model outperformed previous pu learning models in terms of accuracy deng et al. [35] proposed a pu semi-supervised learning model to detect fake reviews based on review content and metadata features. they used the similarity features of the review (duplicate or near duplicate) to detect fake reviews. they then used the k-means algorithm to classify the reviews by calculating the percentage of the fake review in each group. they classified each group depending on its threshold value. they labelled the review positively if the review is far away from the trusted negative case. in contrast, the reviews are negative if the reviews are close to the true negative case. they collected the electronic products dataset from jd.com. the results showed that the proposed model performed well in identifying fake reviews with an 88.1% average accuracy. yet, the proposed model did not perform well with short texts of less than 20 words. furthermore, they did not compare their model with other models to prove the effectiveness. another research was conducted by hai et al. [13], who introduced a multi-task method (smtl-llr) in order to detect fake reviews. laplacian logistic regression (llr) was used to leverage the unlabelled data and introduced a semi-supervised multi-task method via laplacian regularized logistic regression (smtl-llr). the proposed model improved the learning for the single task by using the knowledge covered inside the training of another similar task. they selected 10,000 unlabelled reviews randomly, containing three domains (doctor, hotel, and restaurant) from the datasets created by ott et al. [77]. the experimental results showed that smtl-llr outperforms the state-of-the-art methods [4], [77], [147]–​[149] in three domains (doctor, hotel, and restaurant) with an accuracy of 85.4%,88.7%, and 87.5%, respectively. however, the proposed model ignored the reviewer information, which could improve the classification model performance.

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