## 5 Results and Discussion
### Performance Evaluation Based on Sentiment Classification  
The assessment of techniques with sentiment classification was done using precision, F1-score, recall, and RMSE, considering the IMDB, Yelp 2013, and Yelp 2014 databases. **(a) Assessment with IMDB dataset:** Figure 4 presents the assessment based on sentiment classification using the IMDB database. The assessment with precision is revealed in Figure 4a. For training data = 60%, the precision measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.785, 0.825, 0.855, and 0.895. Additionally, for training data = 90%, the precision measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.852, 0.874, 0.905, and 0.941. The performance improvement in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using precision are 9.458%, 7.120%, and 3.825%. The assessment with recall is revealed in Figure 4b. For training data = 60%, the recall values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.795, 0.835, 0.865, and 0.905. Additionally, for training data = 90%,  
Figure 4: Results based on sentiment classification using IMDB datasets with (**a**) precision (**b**) recall (**c**) F-measure and (**d**) RMSE. the recall values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChOA-based RMDL are 0.865, 0.885, 0.914, and 0.965. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChOA-based RMDL using recall are 10.362%, 8.290%, and 5.284%. The assessment with the F-measure is revealed in Figure 4c. For training data = 60%, the F-measure values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChOA-based RMDL are 0.790, 0.830, 0.860, and 0.900. Additionally, for training data = 90%, the F-measure values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChOA-based RMDL are 0.859, 0.880, 0.910, and 0.953. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChOA-based RMDL using the F-measure are 9.863%, 7.660%, and 4.512%. The assessment with RMSE is revealed in Figure 4d. For training data = 60%, the RMSE values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChOA-based RMDL are 0.485, 0.375, 0.239, and 0.199. Additionally, for training data = 100%, the RMSE values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChOA-based RMDL are 0.395, 0.221, 0.141, and 0.097. **(b) Assessment with Yelp 2013 datasets:** The assessment based on sentiment classification using the Yelp 2013 database is revealed in Figure 5. The assessment considering precision is displayed in Figure 5a. When the data percentage considered for training is 60, the precision values measured by HSACN, MCNN, and DCBVN are 0.754, 0.775, and 0.814, while that of the proposed spider Taylor-ChOA-based RMDL is 0.842. Additionally, when the data percentage considered for training is 90, the precision values measured by HSACN, MCNN, and DCBVN  
Figure 5: Results based on sentiment classification using Yelp 2013 datasets with (**a**) precision (**b**) recall (**c**) F-measure and (**d**) RMSE. are 0.825, 0.841, and 0.885, while that of the proposed spider Taylor-ChoA-based RMDL is 0.933. The performance improvements in HSACN, MCNN, DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using precision are 11.575%, 9.860%, and 5.144%. The assessment with recall is revealed in Figure 5b. When the data percentage considered for training is 60, the recall values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.775, 0.795, 0.833, and 0.875. Additionally, when the data percentage considered for training is 90, the recall values measured by HSACN, MCNN, and DCBVN are 0.833, 0.865, and 0.895, while that of the proposed spider Taylor-ChoA-based RMDL is 0.941.