## 5 Results and Discussion
### Performance Evaluation Based on Review Rating Prediction  
The assessment of techniques with the review rating is done using precision, F1-score, and recall considering the IMDB, Yelp 2013, and Yelp 2014 databases. **(a) Assessment with IMDB dataset:** Figure 7 reveals the assessment considering the review rating with the IMDB database. The assessment considering precision is displayed in Figure 7a. The precision values generated by DNN, CNN+LSTM, Bi-GRU, CNN and the proposed spider Taylor-ChoA-based HAN are 0.635, 0.685, 0.741, 0.845, and 0.865 when the training data are at 60%. Additionally, the precision values measured by DNN, CNN+LSTM, Bi-GRU, CNN and proposed Spider Taylor-ChoA-based HAN are 0.695, 0.765, 0.805, 0.901, and 0.931 for training data = 90%. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using precision are 25.349%, 17.830%, 13.533%, and 3.222%. The assessment with recall is revealed in Figure 7b. The recall values measured by DNN, CNN+LSTM, Bi-GRU, CNN and the proposed spider Taylor-ChoA-based HAN are 0.654, 0.699, 0.754, 0.850, and 0.885 for training data = 60%. Additionally, the recall measured by DNN, CNN+LSTM, Bi-GRU, CNN and the proposed spider Taylor-ChoA-based HAN are 0.724, 0.775, 0.825, 0.928, and 0.954 for training data = 90%. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using recall are 24.109%, 18.763%, 13.522%, and 2.725%. The assessment with the F-measure is revealed in Figure 7c. The F-measure values measured by DNN, CNN+LSTM, Bi-GRU, CNN and the proposed spider Taylor-ChoA-based HAN are 0.645, 0.692, 0.748, 0.847, and 0.875 for training data = 60%. Additionally, the F-measure measured by DNN, CNN+LSTM, Bi-GRU,  
Figure 6: Results based on sentiment classification using Yelp 2014 datasets with (**a**) precision (**b**) recall (**c**) F-measure and (**d**) RMSE. CNN and the proposed spider Taylor-ChoA-based HAN are 0.709, 0.770, 0.815, 0.914, and 0.943 for training data = 90%. The performance improvements in HSACN, MCNN, and DCBVN with respect to the proposed spider Taylor-ChoA-based RMDL using F-measure are 24.814%, 18.345%, 13.573%, and 3.075%. The assessment with RMSE is revealed in Figure 7d. The RMSE values measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.433, 0.325, 0.221, 0.185, and 0.154 for training data = 60%. Additionally, the RMSE measured by HSACN, MCNN, DCBVN, and the proposed spider Taylor-ChoA-based RMDL are 0.314, 0.214, 0.102, 0.085, and 0.075 for training data = 100%. **(b) Assessment with Yelp 2013 datasets:** The assessment based on review rating using Yelp 2013 database is revealed in Figure 8.  
Figure 7: Results based on review rating prediction using IMDB datasets with **(a)** precision **(b)** recall **(c)** F-measure and **(d)** RMSE. The assessment with precision is displayed in Figure 8a. For training data = 60%, the precision of DNN is 0.633, that of CNN+LSTM is 0.665, that of Bi-GRU is 0.704, that of CNN is 0.799 and that of the proposed spider Taylor-ChoA-based HAN is 0.841. Additionally, for training data = 90%, the precision of DNN is 0.741, that of CNN+LSTM is 0.765, that of Bi-GRU is 0.799, that of CNN is 0.895, and that of the proposed Spider Taylor-ChoA-based HAN is 0.925. The performance improvements of DNN, CNN+LSTM, Bi-GRU, and CNN with respect to the proposed spider Taylor-ChoA-based HAN using precision are 19.891%, 17.297%, 13.621%, and 3.243%. The assessment with recall is revealed in Figure 8b. For training data = 60%, the recall of DNN is 0.654, that of CNN+LSTM is 0.699, that of Bi-GRU is 0.741, that of CNN is 0.801, and that of the proposed spider Taylor-ChoA-based HAN is 0.854. Additionally, for training data = 90%, the recall of DNN is 0.769, that of CNN+LSTM is 0.804, that of Bi-GRU is 0.901, and that of the proposed spider Taylor-ChoA-based HAN is 0.935.