## 2 Related Work  
Innumerable techniques are devised for sentiment classification and review rating prediction, but none of the techniques hybridize review rating and sentiment analysis. Thus, the motive is to devise a novel technique for sentiment analysis and review rating. [PERSON] et al. [16] developed a technique, namely InterSentiment, for effective sentiment analysis and review rating. The technique bridged the gap between the sentiment model and user-product interaction model considering the deep model and jointly produced rating scores. However, the technique did not include the combination of semantics in deep models. To combine the semantics of the deep model, [PERSON] et al. [17] developed a technique based on biased user-given ratings based on texts for sentiment analysis. Here, the deep model was devised for examining the textual review. However, the technique did not undergo testing considering various classes and review infrastructures. To include testing with several classes and reviews, [PERSON] and [PERSON] [18] developed the prediction of a review rating with a deep model. The method comprises two stages on the basis of the deep learning bi-directional gated recurrent unit (Bi-GRU) model. Here, the first phase utilized the prediction of polarities, and the second phase was utilized for predicting the review rating using the review text. However, the technique did not adopt the linguistic ruleswhile evaluating the review text. To include the linguistic rules, [PERSON] et al. [19] devised Google App numeric reviews and a ratings contradiction prediction platform with a deep model. The model comprises two phases. Here, the first covers the prediction of polarity in each review considering the sentiment analysis tool and constructed ground truth. Then, in the second phase, the prediction of the star rating is done with a text format after the deep model. The technique faces high computational complexity. To reduce the computational complexity, [PERSON] and [PERSON]. [20] devised a technique for predicting, learning and utilizing the review rating for sentiment analysis. The method considered the preference knowledge for predicting ratings. However, this technique produced poor accuracy. To enhance accuracy,Shrestha [PERSON] and [PERSON]. [21] devised a technique for analyzing sentiment by analyzing the compatibility of Amazon reviews. The analysis of sentiment was done for identifying the reviews using a deep model. Here, the semantic relation of the product data and review text was done to predict the rating score. However, the online review comprises huge noise, which is complicated to analyze. To discard noise, [PERSON] et al. [22] devised a technique for rating reviews. Here, each review is manually annotated based on sentiment polarities. However, this technique cannot handle other databases. To manage other databases and make it flexible, [PERSON] et al. [23] devised a sentiment-aware interactive fusion network (SIFN) for recommending the item. Here, the user or item reviews were encoded with bidirectional encoder representations from transformers (BERT) for mining semantic features. At last, the rating prediction was done for analyzing the final score. The issues confronted by the classical sentiment analysis based on review rating prediction are listed as follows:  
* The SC-based technique concentrates on extracting the content of the review and has the ability to combine user- and product-based features, but it fails to capture adequate interactions among them, expressed in a sparse matrix as collaborative filtering (CF) [16]. * To capture sufficient interactions, the review rating prediction platform with deep model is adapted. Here, the person that considers reviews assists in the rating prediction, with high star ratings being good reviews, but the user's rating star-level information is complex to understand and analyze [18]. * To ease the understanding, the deep model is utilized, wherein the reviews are given in texts.