## 5 Results and Discussion
### Performance Evaluation Based on Sentiment Classification
The performance improvements in HSACN, MCNN, DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using recall are 11.477%, 8.076%, and 4.888%. The assessment with the F-measure is revealed in Figure 5c. When the data percentage considered for training is 60, the F-measure values measured by HSACN, MCNN, and DCBVN are 0.765, 0.785, and 0.823, while that of the proposed spider Taylor-ChoA-based RMDL is 0.858. Additionally, when the data percentage considered for training is 90, the F-measure values measured by HSACN, MCNN, and DCBVN are 0.829, 0.853, and 0.890, while that of the proposed spider Taylor-ChoA-based RMDL is 0.937. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using F-measure are 11.526%, 8.964%, and 5.016%. The assessment with RMSE is revealed in Figure 5d. When the data percentage considered for training is 60, the RMSE values measured by HSACN, MCNN, DCBVN are 0.435, 0.337, and 0.254, while that of the proposed spider Taylor-ChoA-based RMDL is 0.199. Additionally, when the data percentage considered for training is 90, the RMSE values measured by HSACN, MCNN, and DCBVN are 0.335, 0.298, and 0.214, while that of the proposed spider Taylor-ChoA-based RMDL is 0.193. **(c) Assessment with Yelp 2014 datasets:** Figure 6 presents the assessment with a sentiment classification considering the Yelp 2014 database. The assessment with precision is displayed in Figure 6a. For training data = 60%, the precision measured by HSACN is 0.775, MCNN is 0.799, DCBVN is 0.833, and the proposed spider Taylor-ChoA-based RMDL is 0.875. Additionally, for training data = 90%, the precision measured by HSACN is 0.833, MCNN is 0.854, DCBVN is 0.895, and the proposed spider Taylor-ChoA-based RMDL is 0.947. The performance improvements of HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using precision are 12.038%, 9.820%, and 5.491%. The assessment with recall is revealed in Figure 6b. For training data = 60%, the recall measured by HSACN is 0.785, that by MCNN is 0.801, that by DCBVN is 0.854, and that by the proposed spider Taylor-ChoA-based RMDL is 0.885. Additionally, for training data = 90%, the recall measured by HSACN is 0.848, that by MCNN is 0.865, that by DCBVN is 0.903, and that by the proposed spider Taylor-ChoA-based RMDL is 0.955. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using recall are 11.204%, 9.424%, and 5.445%. The assessment with the F-measure is revealed in Figure 6c. For training data = 60%, the F-measure measured by HSACN is 0.780, that by MCNN is 0.800, that by DCBVN is 0.843, and that by the proposed spider Taylor-ChoA-based RMDL is 0.880. Additionally, for training data = 90%, the F-measure measured by HSACN is 0.840, that by MCNN is 0.860, that by DCBVN is 0.899, and that by the proposed spider Taylor-ChoA-based RMDL is 0.951. The performance improvements of HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using the F-measure are 11.671%, 9.568%, and 5.467%.The assessment with RMSE is revealed in Figure 6d. For training data = 60%, the RMSE measured by HSACN is 0.387, that by MCNN is 0.313, that by DCBVN is 0.214, and that by the proposed spider Taylor-ChoA-based RMDL is 0.185. Additionally, for training data = 100%, the RMSE measured by HSACN is 0.303, that by MCNN is 0.241, that by DCBVN is 0.125, and that by the proposed spider Taylor-ChoA-based RMDL is 0.087.