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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. TABLE 3 Comparison of Exiting Feature Extraction Methods SECTION V. 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. B. Neural Network in Detecting Fake Reviews To enhance the classification model performance, the attention neural network method, introduced by Wang et al. [75] to indicate whether a review is behavioural misleading or linguistically misleading, or both. The proposed model used dynamic weight as a form of measure by observing behavioural and linguistic patterns for training. Multi-layer perceptron was used to extract the behavioural features: a CNN to extract linguistic features. Then, the attention method was used to learn the dynamic weight for linguistic and behavioural features. The experimental results on Yelp dataset [8] showed that the proposed model outperformed the state of art methods [8], [76] with an 88.8% accuracy on the Hotel domain and 91% on the Restaurant domain. Furthermore, attention mechanism plays significant role in enhancing the classification model performance. However, the proposed model focused more on linguistic features than behavioural features, which are not enough to identify fake reviews. In this model, in addition to HAN architecture, we included a 1-dimensional convolution layer before each two-way GRU layer in HAN to extract high-level input features. This layer takes the feature of the text review before being fed to the attention layer. Similarly to HAN architecture, we set the maximum length to 200, then, Bi-GRU with 100 output dimensions was fed to the attention layer. Further, we used an ADAM optimizer with a 0.001 learning rate to optimize our model. RoBERTa is an extended version of BERT that can exceed the BERT transformer's performance [179] by training the model longer, training on longer sequences, and removing the next sentence prediction. In addition to English Wikipedia and books corpus, RoBERTa is pre-trained on one more dataset; Common Crawl News datasets containing 63 million news articles in the English language. Similarly, in this research, to encode the inputs in tokens and designate them as input ids, the RoBERTa tokenizer was used. These IDs have been padded to a fixed length to prevent row variation. The characteristics of the sentence pair classification were then extracted from the tokens. faux saint laurent bag
[Image] Promising review: "I bought this for my pool party, but it was a success! It's not as big as the picture, but I like the design. 23. [Image] Promising review: "These are fantastic for the price. They are so lightweight and absorbent, but absorbent enough to clean the tub and shower in a few seconds. " -Aly 24. A portable water fountain that'll let you take a dip in your pool or poolside while soaking in the sun. [Image] Promising review: "I used it for my backyard for my backyard, it was a lot of fun. I can put it inside to store it. faux saint laurent bagfaux saint laurent bag
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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. TABLE 3 Comparison of Exiting Feature Extraction Methods SECTION V. 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. B. Neural Network in Detecting Fake Reviews To enhance the classification model performance, the attention neural network method, introduced by Wang et al. [75] to indicate whether a review is behavioural misleading or linguistically misleading, or both. The proposed model used dynamic weight as a form of measure by observing behavioural and linguistic patterns for training. Multi-layer perceptron was used to extract the behavioural features: a CNN to extract linguistic features. Then, the attention method was used to learn the dynamic weight for linguistic and behavioural features. The experimental results on Yelp dataset [8] showed that the proposed model outperformed the state of art methods [8], [76] with an 88.8% accuracy on the Hotel domain and 91% on the Restaurant domain. Furthermore, attention mechanism plays significant role in enhancing the classification model performance. However, the proposed model focused more on linguistic features than behavioural features, which are not enough to identify fake reviews. In this model, in addition to HAN architecture, we included a 1-dimensional convolution layer before each two-way GRU layer in HAN to extract high-level input features. This layer takes the feature of the text review before being fed to the attention layer. Similarly to HAN architecture, we set the maximum length to 200, then, Bi-GRU with 100 output dimensions was fed to the attention layer. Further, we used an ADAM optimizer with a 0.001 learning rate to optimize our model. RoBERTa is an extended version of BERT that can exceed the BERT transformer's performance [179] by training the model longer, training on longer sequences, and removing the next sentence prediction. In addition to English Wikipedia and books corpus, RoBERTa is pre-trained on one more dataset; Common Crawl News datasets containing 63 million news articles in the English language. Similarly, in this research, to encode the inputs in tokens and designate them as input ids, the RoBERTa tokenizer was used. These IDs have been padded to a fixed length to prevent row variation. The characteristics of the sentence pair classification were then extracted from the tokens. faux saint laurent bag
[Image] Promising review: "I bought this for my pool party, but it was a success! It's not as big as the picture, but I like the design. 23. [Image] Promising review: "These are fantastic for the price. They are so lightweight and absorbent, but absorbent enough to clean the tub and shower in a few seconds. " -Aly 24. A portable water fountain that'll let you take a dip in your pool or poolside while soaking in the sun. [Image] Promising review: "I used it for my backyard for my backyard, it was a lot of fun. I can put it inside to store it. faux saint laurent bagfaux saint laurent bag
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