## 3 Approaches
### Deep Learning  
The idea of the deep learning concept came from the way the human brain function to process data and generate patterns for decision making. Deep learning is known to be a subset of machine learning that is capable of preform classification tasks on different data types, namely text, images, videos, and sounds. Recently, deep learning techniques gain attention in different applications, especially spam reviews detection. Due to its ability to learn from big data, many authors proposed various approaches based on deep learning to solve spam review detection. One example for this is the work in [137], where a Convolutional Neural Network (CNN) is presented. CNN is applied in order to identify the semantic information of users reviews that can enhance the detection of deceptive characteristics. When compared with other neural network architectures' performance. The proposed CNN operates better in detecting spam reviews. Similar to the prior work, the authors of [138] also argue on the superiority of deep learning techniques in extracting the semantic aspect of reviews context. Their new approach (Paragraph Vector Distributed Bag of Words (PV-DBOW)) can recognize the global representation of reviews semantics. Furthermore, the representations of reviews transfer to a neural network approach to detect spam reviews. The experiments show that the PV-DBOW outperforms the existing state-of-the-art methods. On the other hand, [PERSON]. [139] introduced a novel multi-dimensional features approach for spam reviews detection. The approach used the standard component in order to obtain the low-dimensional features to classify the user-product connection. Long short-term memory (LSTM) is applied and trained with a capsule network to identify the spatial structure and textual context features. Moreover, the model merges the user behavioral and text features to be utilized as input for the detection classification module. The outcome of the approach proves its ability to detect spam reviews more than the existing methods. Besides, the study of [140] debates the lack of labeled dataset availability in spam reviews. Consequently, the authors suggested LSTM networks based on unsupervised learning to discriminate the spam reviews from the real ones. The model is trained to discover the patterns of real reviews without the required labels. The experimental results reveal that their framework can distinguish spam from real reviews efficiently. [PERSON] and [PERSON] [141] also employed the LSTM to study the semantic features of spam reviews. The authors used Deep Belief Network to detect the credibility of product reviews and CNN for discrete features extraction. Thus, connecting the traditional features with semantic features to build a DBN model. The performance of deep confidence network model performs better compared with the standard machine learning methods. Likewise, the work in [142] expressed its thoughts about how the deficiency of traditional machine learning methods to detect spam reviews given limited feature representations. As a result, a Deep Learning (DL) framework is presented to detect spam reviews. The DL framework combined with Self Attention-based CNN BiLSTM (ACB) to extract and identify the document representation of such reviews. Further, the weights of each word are calculated and learning the spamming clues in the sentence and document, respectively. Afterwords. the model studies the sentence representation through CNN as well as discovers the n-gram features of a higher level. In the end, the vectors of sentences are combined by using Bi-directional LSTM in order to spam reviews detection based on contextual information. The experimental results show that the ACB achieved the highest results compared with other variants methods in terms of accuracy. The works that were previously stated in this subsection indicates and demonstrate the powerful performance of deep learning techniques to identify the semantic structures alongside the hidden pattern of spam reviews.