## 5 Experiment  
Our targets were to perform several experiments for the sentiment analysis. As indicated in Figure 1, the input was paragraphs or sets of content-related sentences \(s(i)\), which came from four different text classification datasets: SST-5 [39], IMDB [40], Sentiment140 [41], and AMAZON [42]. All our experiments were conducted on an NVIDIA GeForce RTX 2080 Ti with 11.0 GB of video memory. We first evaluated the proposed multilayer CARU framework with related methods; we also re-implemented the multilayer MTGRU [43] and RNN-LM [44] in PyTorch and compared it with CARU with different numbers of layers used. Furthermore, we evaluated the performance on the same environment with and without the use of the Chebyshev pooling, and with the same configuration and the same number of neural nodes as indicated in Tables 1 and 2. On the other hand, to compare the proposed model with the original networks, we also re-implemented several state-of-the-art baselines found in recent years--L-MIXED [45], EFL [46], and Variable-Depth [3]--, also including the traditional models CNN+LSTM [47] and CEN-tpc [48] in PyTorch. All experiments used the same dataset in each test with a batch size of 100 per iteration set, and with the same configuration. In addition, there \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **MTGRU [43]** & **RNN-LM [44]** & **Multilayer CARU** \\ \hline
1 & \(0.6438\pm 0.003\) & \(0.6612\pm 0.001\) & \(0.6751\pm 0.002\) \\
2 & \(0.6443\pm 0.004\) & \(0.6642\pm 0.001\) & \(0.6759\pm 0.001\) \\
3 & \(0.6515\pm 0.003\) & \(0.6665\pm 0.002\) & \(0.6808\pm 0.007\) \\
4 & \(0.6553\pm 0.004\) & \(0.6736\pm 0.003\) & \(0.6888\pm 0.004\) \\ \hline Chebyshev Pooling & \(0.7051\pm 0.002\) & \(0.7242\pm 0.005\) & \(0.7303\pm 0.004\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average accuracy of SST-5 test data with the error range. was a scheduler for adjusting the learning rate. It reduced the learning rate when the loss became stagnant. The comparison results are given in Table 3; it can be seen that the proposed model was better than the others used in their studies. While 80.90% and 66.49% accuracy values were achieved for the Sentiment140 and AMAZON datasets, respectively, by a gap of 1.0%, in contrast, the result of the IMDB test with the proposed model was very close to the best, but did not exceed it. The reason was that the contents of the IMDB dataset were mainly comments, which tend to be mostly neutral and objective, and it was difficult to reflect the expression of emotion. Based on these results, it could be inferred that the proposed model was at least as successful as, or even better than, the other existing models. Corresponding to Table 3, Figure 4 shows the complete tendency of accuracy for the selected datasets trained by the various model. Obviously, these accuracy values were usually unstable before 60 epochs, and they were quite stable after 60 epochs. The reason we considered this was that the proposed model first prioritized to train the multilayer CARU to extract some useful features, so the difference was always presented with a larger value in order to contribute a larger gradient for the rollback used. Once the training of the multilayer CARU was almost complete, the gradient would only be contributed by the faster and more stable CNN-based extractor like [49]. Therefore, the training in the later stage became stable and converged faster. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer** & **MTGRU [43]** & **RNN-LM [44]** & **Multilayer CARU** \\ \hline
1 & \(0.8853\pm 0.003\) & \(0.8923\pm 0.004\) & \(0.8971\pm 0.005\) \\
2 & \(0.8923\pm 0.006\) & \(0.8957\pm 0.006\) & \(0.9045\pm 0.009\) \\
3 & \(0.8953\pm 0.012\) & \(0.9034\pm 0.003\) & \(0.9098\pm 0.007\) \\
4 & \(0.8992\pm 0.007\) & \(0.9038\pm 0.005\) & \(0.9157\pm 0.006\) \\ \hline Chebyshev Pooling & \(0.9292\pm 0.006\) & \(0.9318\pm 0.004\) & \(0.9459\pm 0.003\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average accuracy of IMDB test data with the error range.