## 6 Conclusions and Future Work  
This research aims to develop a method on the basis of sentiment classification for review rating prediction. The review data undergo feature extraction, wherein imperative features, such as SentiWordNet-based statistical features, TF-IDF-based features, the number of capitalized words, numerical words, punctuation marks, elongated words, hashtags, emoticons, and number of sentences, are extracted. Then, the spider Taylor-ChOA-based RMDL is employed for classifying the sentiment. The training of RMDL is done using the proposed spider Taylor-ChOA, which is obtained by combining the SMO and Taylor-ChOA. The spider Taylor-ChOA-based HAN is employed for predicting the review rating. The training of HAN is done using the proposed spider Taylor-ChOA, which is obtained by combining the SMO and Taylor-ChOA. We conduct extensive experiments on different datasets, and the experiment results show that the proposed method outperforms state-of-the-art methods of review rating prediction tasks. The features mined are employed in the review rating prediction, which helps to devise the review as being positive and negative using HAN. The proposed spider Taylor-ChOA-based RMDL provides enhanced performance with the highest precision of 94.1%, recall of 96.5%, and highest F-measure of 95.3%. The proposed spider Taylor-ChOA-based HAN offers improved performance with the highest precision of 93.1%, highest recall of 95.4% and the highest F-measure of 94.3%. The proposed method is limited in extracting capitalized words, numerical words, punctuation marks, hash tags, number of sentences, emoticons and elongated words with their proper sentiments. In the future, we plan to enhance the pre-processing with word embeddings by exploiting deep recurrent neural networks. We can also extend this research via convolution neural networks. Additionally, other datasets can be adapted to check the feasibility of the proposed model.  
[PERSON] designed and wrote the paper; [PERSON] supervised the work; [PERSON] performed the experiments with advice from [PERSON]; and [PERSON] organized and proofread the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in the experiments are publicly available. Details are given in Section 4.1.  
The authors appreciate and acknowledge anonymous reviewers for their reviews and guidance.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**IMDB**} & \multicolumn{2}{c}{**Yelp 2013**} & \multicolumn{2}{c}{**Yelp 2014**} \\  & **ACC** & **RMSE** & **ACC** & **RMSE** & **ACC** & **RMSE** \\ \hline Trigram+UPF & 0.404 & 1.764 & 0.570 & 0.803 & 0.576 & 0.789 \\ \hline TextFeature UPF & 0.402 & 1.774 & 0.561 & 1.822 & 0.579 & 0.791 \\ \hline JMARS & N/A & 1.773 & N/A & 0.985 & N/A & 0.999 \\ \hline UPNN (CNN) & 0.435 & 1.602 & 0.596 & 0.784 & 0.608 & 0.764 \\ \hline UPNN (NSC) & 0.471 & 1.443 & 0.631 & 0.702 & N/A & N/A \\ \hline NSC+UPA & 0.533 & 1.281 & 0.650 & 0.692 & 0.667 & **0.654** \\ \hline HSACN & 0.852 & – & 0.825 & – & 0.833 & – \\ \hline MCNN & 0.874 & – & 0.841 & – & 0.854 & – \\ \hline DCBVN & 0.905 & – & 0.885 & – & 0.895 & – \\ \hline Proposed Method & **0.941** & – & **0.933** & – & **0.947** & – \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of proposed spider Taylor-ChOA-based HAN with existing methods, in terms of accuracy (ACC) and RMSE. The best results are displayed in boldface.