## 2 Related Work  
Review analysis is a representative application area of sentiment analysis [22]. Sentiment analysis, also referred to as sentiment classification, aims to extract opinions from a large number of unstructured texts and classify them into sentiment polarities, positive, neutral, or negative [23]. Conventional approaches usually focused on textual data [24]. [PERSON] et al. [21] surveyed 112 papers discussing sentiment analysis and showed that LSTM (33.5%) is the most commonly used for text analysis, followed by GRU (8.77%), RNN (7.89%), and BERT (3.07%). With the advances in social media, it is significant to precisely capture the sentiment via the presence of different modalities (i.e., textual, acoustic, and visual) [25]. From this, recent research has focused more on aspect-based multimodal sentiment analysis [26; 27; 28]. In the case of review analysis, most of the existing studies have still focused on textual data, aiming to determine whether a given review is spam or ham (legitimate review). Table 1 summarizes the existing studies related to the review analysis in terms of source (learning) data, machine learning algorithm with the best performance, and prediction performance. As mentioned before, the majority of the existing studies discussed the detection of Type 1 reviews. [PERSON] et al. [29] obtained hotel reviews through Amazon Mechanical Turk (AMT), a crowdfunding site, and extracted LIWC (Linguistic Inquiry and Word Count) and bigrams from the reviews. Using the features, they achieved an accuracy of 0.898 using SVM. [PERSON] et al. [30] adopted the stylometric feature, a mixture of lexical and syntactic features of the reviews, and obtained an F1 score of 0.84 using SVM for the hotel reviews presented by [PERSON] et al. [PERSON] et al. [31] extracted Context Free Grammar (CFG) parse trees from the hotel reviews and obtained an accuracy of 0.912 using SVM. [PERSON] et al. [32] extracted review sentiments (positive or negative) and reviewers' behaviors from 6000 reviews collected from Epinions and obtained an F1 score of 0.631 using Naive Bayes. [PERSON] et al. [PERSON] et al. [33] suggested that higher accuracy can be achieved using the reviewers' abnormal behavioral features together with the linguistic features of the reviews, and their SVM model showed an accuracy of 0.861 for the Yelp data. [PERSON] et al. [34] tried to increase accuracy by using reviewers' temporal and spatial patterns and obtained an F1 score of 0.85 using SVM. Recently, neural networks have been used more frequently to achieve higher accuracy. [PERSON] [PERSON] and [PERSON] and [PERSON] [35] obtained an accuracy of 0.89 using DNN (Deep Neural Network) for the hotel reviews of [PERSON] et al. [PERSON] et al. Reference [36] employed CNN (Convolutional Neural Network) [37] for the dataset presented by [31] and achieved an F1 score of 0.823. [PERSON] et al. [38] also used CNN and achieved an F1 score of 0.828 for 24,166 hotel reviews collected online. [PERSON] et al. [PERSON] et al. [39] showed that the LSTM model provided better performance than the CNN model and achieved an F1 score of 0.946 using LSTM for the hotel reviews discussed in the work of [PERSON] et al., while [PERSON] et al. [40] used bidirectional LSTM with feature combination and obtained an F1 score of 0.876 for the dataset from [31]. Several studies have attempted to combine two or more algorithms for better performance. [PERSON] et al. [PERSON] et al. [41] adopted Attention [42] to express the relationship between two features, in addition to CNN to express linguistic features. Their algorithm provided an F1 score of 0.889 and 0.912 for the hotel and restaurant reviews in the Yelp data, respectively. [PERSON] and [PERSON] [PERSON] and [PERSON] [43] combined CNN and bidirectional RNN and obtained an F1 score of 0.839 for the dataset from [31]. [PERSON] et al. [44] used CNN and LSTM to build a document vector and obtained an F1 score of 0.87 for the YelpZip data. [PERSON] et al. [PERSON] et al.