## 2 Related Work  
A literature survey of the existing Sentiment Classification and review rate prediction model is explicated in this section. [PERSON] et al. [17] devised an attention-based Bi-GRU neural network model for Sentiment Classification. Here, the word2 vec model was applied for word vector description, and an attention method was exploited for sentiment analysis. The prediction accuracy was high in this method. However, it failed to include fine-grained machine learning schemes for detecting an unpredictable review. [PERSON] and [PERSON] [12] presented sequence encoding with a CNN model for the Sentiment Classification process. The dependency-graph-driven position encoding was improved with weighted sentiment features, and it was combined with feature representation. Moreover, conventional word embedding features were integrated with an encoded sentiment sequence for a revised DCNN for Sentiment Classification. The classification performance was highly enhanced, even though this model failed to embrace large and inclusive data for performing empirical experiments. [PERSON] and [PERSON] [18] modeled a word embedding and deep learning approach for Sentiment Classification. Here, three various kinds of predictive schemes, deep learning, machine learning and transfer learning, were applied. [PERSON] and [PERSON] [7] designed sentiment analysis using machine learning and deep learning. Here, polarity-based sentiment analysis and deep learning models were used to classify the user's tweets as having positive or negative sentiment. Deep learning models have been implied to classify the tweets. [PERSON] et al. [8] devised sentiment analysis using machine learning algorithms. Here, text mining techniques were created to generate and process the variables. In addition, they created a classifier to classify tweets into positive and negative sentiments to evaluate the performance of the model. This method achieved greater precision. Furthermore, an unsupervised technique was exploited for aspect-level Sentiment Classification. Moreover, it permitted the ability of pre-trained language methods and removed more difficulties linked to supervised learning methods. The computational complexity was lower in this method, even though it failed to resolve mislabeled review issues. [PERSON], [PERSON] et al. [3] presented a Spider Monkey Crow Optimization Algorithm (SMCA), along with a Deep Recurrent Neural Network (DeepRNN), for Sentiment Classification and information retrieval. Stemming and stop word removal were applied for eradicating undesirable data, and feature extraction was also carried out for the classification process. Fuzzy K-Nearest Neighbor (Fuzzy KNN) was utilized for retrieving data using a distance measure. [PERSON], [PERSON] et al. [19] devised InterSentiment through connecting the model user-product interaction and sentiment methods by means of deep learning. This method learns about high-level depictions by linking review sentiment and the user-product interaction, which was used for rate scoring. The error rate was highly reduced, although it failed to perform other recommendation tasks. [PERSON], [PERSON] et al. [4] presented a deep learning model for Review Rating Prediction. Here, a biased rating was elucidated and the matching rules were also inclined. Moreover, data pre-processing was carried out and then feature extraction was carried out for review prediction. This approach obtained a better prediction performance, but the user had a limited amount of reviews. [PERSON], [PERSON] and [PERSON] [20] designed a deep learning technique for Review Rating Prediction. This approach mainly comprised two segments, namely the Bi-GRU technique and a deep learning bidirectional gated recurrent unit. Here, the first section contains polarity prediction, and the second phase employed a review rating process from review data. The processing time was lower in this method, although it did not use other languages. [PERSON], [PERSON] et al. [21] introduced a deep learning approach for Review Rating Prediction.