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Yelp CHI is a real-world dataset, from 2004 to 2012, collected by Mukherjee et al. [8], which contains 67,365 reviews of both restaurants and hotels in Chicago city. Reviews were labelled as either fake or genuine by the Yelp spam filter. The authors used behavioural features and lexical features to learn classifiers. User behaviour features were explicitly collected by analysing website ads and internal data, such as geographic location information, user IP address, session logs, and network. By following the same method, two more real-world datasets, Yelp NYC and Yelp ZIP, were collected from Yelp.com by Rayana and Akoglu [72] from 2004 to 2015 where Yelp NYC contains 359.052 reviews and Yelp Zip contains 608.598. Similarly, each review was labelled as fake or genuine by the Yelp spam filter. The average length for Yelp datasets is 130.6. Later, Li et al. [80] constructed datasets in the Chinese language using a Dianping filtering algorithm where the average review length is 85.5. This dataset includes 9.765. reviews. However, these datasets were built based on an unknown filtering algorithm to label each review as fake or genuine, and these algorithms are not available publicly. Supervised learning techniques are used to predict if reviews are fake or not. This sub-section shall sum up the existing supervised learning techniques in the literature shown in Table 5. For example, Jindal and Liu [4] introduced a supervised learning algorithm to detect fake reviews by studying duplicate reviews. The proposed model consisted of two phases. The first phase used unigram and bigram as features, with Naïve Bayes, Random forest, and support vector machine utilized as a classification algorithm. The second phase used two ensemble methods (stacking and voting) to enhance the classification methods performance. The results on the AMT dataset [77] showed that the ensemble techniques gave better results than the Naïve Bayes random forest and SVM classification algorithms. Using the simple feature and ensemble methods can enhance the accuracy in detecting fake reviews. However, it can be unreliable if duplicate reviews are considered to be fake reviews. Similarly, Lin et al. [12] introduced a classification model to detect fake reviews in a cross-domain environment based on a Sparse Additive Generative Model (SAGE), which is created based on the Bayesian generative model [136]. The model is a combination of a generalized additive model and topic modelling [137]. They used linguistic query and word account (LIWC), POS, and unigram techniques as features to detect fake reviews in cross-domains. The proposed model could capture different aspects such as fake vs. truthful and positive vs. negative. They used the AMT dataset [77] which consisting of three domain reviews (Hotels, Doctors, and Restaurants) to evaluate the proposed model. The experimental results showed that the accuracy of the classification using unigram was 65%. The accuracy of two class classifications (Turker and Employee reviews) using unigram was 76.1%. The accuracy on cross-domain using unigram, POS, and LIWC separately were 77%, 74.6%, and 74.2%, respectively, on the restaurant domain. The accuracy on cross-domain using unigram, POS, and LIWC separately using Doctor domain were: 52%, 63.4%, and 64.7%. However, the proposed model failed in capturing the semantic information of the sentence. In related work, Hernández-Castañeda et al. [29] investigated the efficiency of using SVN (Support Vector Network) in classification tasks to detect fake reviews in one, mixed and cross-domains. They used the LIWC, Word space model (WSM), and latent Dirichlet Allocation (LDA) techniques as a feature extraction method. They evaluated the proposed model on three datasets; the DeRev dataset [89], OpSpam dataset [77] and Opinions dataset [138]. The results compared to the previous works [77], [89], [138] showed that a combination of WSM and LDA achieved the best results in one domain with an accuracy of 90.9% on the OpSpam dataset, 94.9% on DeRev dataset, 87.5% on Abortion dataset, 87% on Best Friend dataset and 80% on Death Penalty dataset. There was also an accuracy of 76.3% in a mixed domain compared to the Naïve Bayes classifier. However, the proposed model did not achieve the best results on cross-domain compared to state-of the-art methods. The performance was good in one domain and mix domain and poor in cross-domain because they used the dataset for testing and combined the remaining dataset for training. This suggests that a deep neural network is probably more appropriate to improve fake review detection in a cross-domain by improving the learning presentation. In this section, a first-hand evaluation of the performances of seven promising deep learning algorithms on two datasets is presented. These algorithms are character-level convolutional -LSTM, convolutional -LSTM, HAN, convolutional HAN, BERT, DistilBERT, and RoBERTa. The main goal is investigating to what extent such algorithms are able to detect fake reviews. Note that, some of these algorithms have been used by researchers in different domains [174]–​[179]. However, as of yet, they have not been used in the fake review detection field. Therefore, this study demonstrated the efficiency of such algorithms in detecting fake review, which can pose as a baseline for further research. For the initial experiments in this study, we used two datasets. The first dataset is the "Yelp Consumer Electronic dataset" [79] that crawled through review datasets based on the web-scraper process from Yelp.com. They labelled them based on content and user behavioural features. This dataset was annotated based on the rule-based method. For example, the dataset was constructed on some rules that considered the review as a fake if different/ same users posted reviews of the different/ same product. This dataset presents a real-life dataset which is preferred as this will help the researchers to build a fake review detection model that can be used efficiently in the real world. A second dataset is the "deception dataset" [100] constructed from TripAdvisor and Amazon Mechanical Turk websites from Chicago city, which contains 3,032 reviews from different domains (Hotel, Restaurant, and Doctor) by crowdsourcing platform. This dataset has extensively used in literature, and it is semi-real dataset [3], [4], [12], [27], [29], [32], [37], [65]. For simplicity, we combined these three-domain reviews at current stages, and we leave the investigation of each domain separately (i.e., multi-domain detection model) for future work. As can be seen from the previous section, to design a fake review detection model, the following steps are performed. During this phase, datasets were pre-processed in order to eliminate the noise, such as stop words, URLs, emojis, etc. The pre-processing has been carried out with the NLTK toolkit,1 an open-source library commonly used. First, we used tokenization to divide the text into a list of tokens; then, we removed the stop words that cause noise in text classification. Finally, we used the stemming method to reduces the words to their root. Table 12 shows the information of reviews in the deception dataset and yelp consumer electronic datasets. For simplicity, we combined the three-domains reviews in the deception dataset. In this section, we specifically discuss the performance analyses of deep learning models and transformers architectures. To do these experiments, we used the same parameters according to the original proposed architecture. We divided each dataset into training, validation and testing to perform the experiments. Based on these predefined parameters, evaluate these algorithms performance in fake review detection in terms of performance accuracy, precision, recall, and F1-score as described in Table 13. They offer classic blackjack titles, demo mode, and several other blackjack variations and side bets, like Perfect Pairs, to try as you gain confidence in the game. The best variant for casual play is the original 21 blackjack. Chrome and Safari are both high-quality browsers for online gaming. Just ensure your password is strong/secure. 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