## 1 Introduction
Usually, Twitter Sentiment Classification observes machine learning schemes in order to formulate a classifier from tweets by means of polarity labels of physically annotated sentiments [3; 13]. Commonly, there are three techniques tat are utilized for sentiment analysis, which are the lexicon-based model, such as unsupervised schemes [14], machine learning, such as the supervised method [15], and the hybrid technique, including both unsupervised and supervised schemes. Various approaches by means of deep learning, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs), are more effectual in the text Sentiment Classification process. The integrated machine learning approaches with the deep learning model offered an improved Sentiment Classification performance. Moreover, the Sentiment Classification scheme, depending on the deep learning scheme, utilizes end-to-end patter and also executes classifier and text representation using a neural network [16]. Additionally, preceding approaches for sentiment analysis trained the shallow methods for attaining acceptable polarity classification outcomes. Moreover, these schemes are employed in conventional classification systems, including Support Vector Machines (SVM), Latent Dirichlet Allocation (LDA) and Naive Bayes, on linguistic features, such as Part-of-Speech (POS) tags, n-grams and lexical features. The major intention is to devise a Sentiment Classification and Review Rating Prediction technique. Here, review data were taken as the input and pre-processing was performed using a stop word removal and stemming process. Then, most significant features, including SentiWordNet-based statistical features, Term Frequency-Inverse Inverse Document Frequency (TF-IDF)-based features, the number of capitalized words, punctuation marks, numerical words, elongated words, emoticons, hashtags and the number of sentences were mined in the feature extraction stage to enhance classification. Afterwards, the extracted features were passed to the HAN and GRU for Review Rating Predictions and the classification process, respectively. Moreover, the HAN was trained by Jaya TDO algorithm, and the GRU was trained based on TD-Spider Taylor ChOA. The major contributions of the paper are as follows:  
* **Designed TD-Spider Taylor ChoA model for Sentiment Classification:** The GRU architecture was applied for performing Sentiment Classification. Additionally, the GRU model was trained by a developed optimization algorithm, named TD-Spider Taylor ChoA, which is newly designed by the combination of ChoA, Taylor series, TDO and SMO algorithms. * **Developed Jaya TDO-HAN approach for Review Rating Prediction:** Here, the HAN model was applied for predicting review ratings from extracted features. Moreover, the HAN model was trained by the proposed Jaya-TDO algorithm. Accordingly, the designed Jaya-TDO was designed by integrating Jaya optimization and the TDO algorithm. The remainder of the paper is organized as follows: Section 2 describes conventional Sentiment Classification and Review Rating Prediction methods. In Section 3 we briefly introduce the architecture of the proposed framework. The systems' implementation and evaluation are described in Section 4, and results and discussions are summarized in Section 5. Finally, Section 6 concludes the overall work and future research studies.