## 5 Results and Discussion
### Performance Evaluation Based on Review Rating Prediction
The performance improvements of DNN, CNN+LSTM, Bi-GRU, and CNN with respect to the proposed spider Taylor-ChoA-based HAN using precision are 17.754%, 14.010%, 10.053%, and 3.636%. The assessment with the F-measure is revealed in Figure 8c. For training data = 60%, the F-measure of DNN is 0.643, that of CNN+LSTM is 0.682, that of Bi-GRU is 0.722, that of CNN is 0.800, and that of the proposed spider Taylor-ChoA-based HAN is 0.848. Additionally, for training data = 90%, the F-measure measured by DNN is 0.755, that of CNN+LSTM is 0.784, that of Bi-GRU is 0.819, that of CNN is 0.898, and that of the proposed spider Taylor-ChoA-based HAN is 0.930. The performance improvement of DNN, CNN+LSTM, Bi-GRU, CNN with respect to proposed Spider Taylor-ChoA-based HAN using F-measure are 18.817%, 15.698%, 11.935%, 3.440%.The assessment with RMSE is revealed in Figure 8d. For training  
Figure 8: Results based on review rating prediction using Yelp 2013 datasets with (**a**) precision (**b**) recall (**c**) F-measure and (**d**) RMSE. data = 60%, the RMSE measured by DNN is 0.485, CNN+LSTM is 0.352, Bi-GRU is 0.303, CNN is 0.225, and proposed Spider Taylor-ChoA-based HAN is 0.187. Also, for training data = 100%, the RMSE measured by DNN is 0.315, CNN+LSTM is 0.275, Bi-GRU is 0.235, CNN is 0.175, and proposed Spider Taylor-ChoA-based HAN is 0.112. **(c) Assessment with Yelp 2014 Datasets:** Figure 9 reveals the assessment considering review rating using Yelp 2014 database. The assessment using precision is displayed in Figure 9a. Considering training data = 60%, the highest precision of 0.841 is generated by the proposed spider Taylor-ChoA-based HAN, while the precision values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.641, 0.695, 0.741, 0.785. Additionally, for training data = 90%, the highest precision of 0.914 is generated by the proposed spider Taylor-ChoA-based HAN, while the precision values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.725, 0.799, 0.841, and 0.895. The performance improvements of DNN, CNN+LSTM, Bi-GRU, and CNN with respect to the proposed spider Taylor-ChoA-based HAN using precision are 20.678%, 12.582%, 7.986%, and 2.078%. The assessment with recall is revealed in Figure 9b. For training data = 60%, the highest recall of 0.854 is generated by proposed spider Taylor-ChoA-based HAN, while the recall values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.665, 0.714, 0.754, and 0.801. Additionally, for training data = 90%, the highest recall of 0.933 is measured by the proposed spider Taylor-ChoA-based HAN, while the recall values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.733, 0.814, 0.854, and 0.912. The performance improvements of DNN, CNN+LSTM, Bi-GRU, and CNN with respect to the proposed spider Taylor-ChoA-based HAN using recall are 21.436%, 12.754%, 8.467%, and 2.250%. The assessment with the  
Figure 9: Results based on review rating prediction using Yelp 2014 datasets with **(a)** precision (b)** recall (c) F-measure and **(d)** RMSE. F-measure is revealed in Figure 9c. For training data = 60%, the highest F-measure of 0.848 is generated by the proposed spider Taylor-ChOA-based HAN, while the F-measure values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.653, 0.704, 0.748, and 0.793. Additionally, for training data = 90%, the highest F-measure of 0.923 is generated by the proposed spider Taylor-ChOA-based HAN, while the F-measure values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.729, 0.806, 0.848, and 0.904. The performance improvements of DNN, CNN+LSTM, Bi-GRU, and CNN with respect to the proposed spider Taylor-ChOA-based HAN using the F-measure are 21.018%, 12.876%, 8.125%, and 2.058%. The assessment with RMSE is revealed in Figure 9d. For training data = 60%, the smallest RMSE of 0.153 is measured by the proposed spider Taylor-ChOA-based HAN while the RMSE values of DNN, CNN+LSTM, Bi-GRU, and CNN are 0.451, 0.333, 0.285, and 0.201.