## 5 Results and Discussion
### Comparative Discussion  
Table 2 describes the assessment of techniques with sentiment classification. Using the IMDB database, the highest precision of 0.941 is measured by the proposed spider Taylor-ChOA-based RMDL while the precision values of HSACN, MCNN, and DCBVN are 0.852, 0.874, and 0.905. The highest precision is measured by the proposed spider Taylor-ChOA-based RMDL, as the RMDL contains the amalgamation of deep models, which helps to increase the precision. The highest recall of 0.965 is measured by the proposed spider Taylor-ChOA-based RMDL, while the recall values of HSACN, MCNN, and DCBVN are 0.865, 0.885, and 0.914. The highest recall reveals that the technique returns the most pertinent results. The highest F-measure of 0.953 is measured by the proposed spider Taylor-ChOA-based RMDL, while the recall values of HSACN, MCNN, and DCBVN are 0.859, 0.880, and 0.910. The highest F-measure reveals that the technique is effective in handling the uneven class. The smallest RMSE of 0.097 is measured by the proposed spider Taylor-ChOA-based RMDL, while the RMSE values of HSACN, MCNN, and DCBVN are 0.395, 0.221, and 0.141. Using the Yelp 2013 database, the highest precision of 0.933, highest recall of 0.941, highest F-measure of 0.937, and smallest RMSE of 0.193 are measured by the proposed spider Taylor-ChOA-based RMDL. Using the Yelp 2014 database, the highest precision of 0.947, highest recall of 0.955, highest F-measure of 0.951, and smallest RMSE of 0.087 are measured by the proposed spider Taylor-ChOA-based RMDL. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Datasets** & **Metrics** & **HSACN** & **MCNN** & **DCBVN** & **Proposed Method** \\ \hline \multirow{4}{*}{IMDB} & Precision & 0.852 & 0.874 & 0.905 & **0.941** \\  & Recall & 0.865 & 0.885 & 0.914 & **0.965** \\  & F-measure & 0.859 & 0.880 & 0.910 & **0.953** \\  & RMSE & 0.39559 & 0.221 & 0.141 & **0.097** \\ \hline \multirow{4}{*}{Yelp 2013} & Precision & 0.825 & 0.841 & 0.885 & **0.933** \\  & Recall & 0.833 & 0.865 & 0.895 & **0.941** \\  & F-measure & 0.829 & 0.853 & 0.890 & **0.937** \\  & RMSE & 0.335 & 0.298 & 0.214 & **0.193** \\ \hline \multirow{4}{*}{Yelp 2014} & Precision & 0.833 & 0.854 & 0.895 & **0.947** \\  & Recall & 0.848 & 0.865 & 0.903 & **0.955** \\ \cline{1-1}  & F-measure & 0.840 & 0.860 & 0.899 & **0.951** \\ \cline{1-1}  & RMSE & 0.859 & 0.880 & 0.910 & **0.953** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of proposed spider Taylor-ChOA-based RMDL with existing methods based on sentiment classification, in terms of precision, recall, F-measure and RMSE. The best results are displayed in boldface. Table 3 displays the assessment with the review rating prediction by altering the training data. Using the IMDB database, the highest precision of 0.931 is measured by the proposed spider Taylor-ChoA-based HAN, while the precision values evaluated by DNN, CNN+LSTM, Bi-GRU, and CNN are 0.695, 0.765, 0.805, and 0.901. The highest precision is due to the HAN, which helps to make an effective review rating. The highest recall of 0.954 is measured by the proposed spider Taylor-ChoA-based HAN, while the recall values evaluated by DNN, CNN+LSTM, Bi-GRU, and CNN are 0.724, 0.775, 0.825, and 0.928. The highest recall reveals that the proposed spider Taylor-ChoA-based HAN is effective in making a relevant prediction. The highest F-measure of 0.943 is measured by the proposed spider Taylor-ChoA-based HAN, while the F-measure values evaluated by DNN, CNN+LSTM, Bi-GRU, and CNN are 0.709, 0.770, 0.815, and 0.914. The F-measure tends to be high when the precision and recall are high. The smallest RMSE of 0.075 is measured by the proposed spider Taylor-ChoA-based HAN, while the RMSE values evaluated by DNN, CNN+LSTM, Bi-GRU, and CNN are 0.314, 0.214, 0.102, and 0.085. Using the Yelp 2013 database, the highest precision of 0.925, highest recall of 0.935, highest F-measure of 0.930 and smallest RMSE of 0.112 are measured by the proposed spider Taylor-ChoA-based HAN.