# A Deep Learning Approach for Sentiment Analysis of COVID-19 Reviews
## 5 Results and Discussion
It can be observed that Leaky ReLU has better accuracy, recall, and F-score results compared to other activation functions considered: TANH, sigmoid, and ReLU. Leaky ReLU is an activation function that influences forward and backward training in LSTM-RNN and controls error using a backpropagation approach. This mechanism potentially has a direct impact on the proposed model's classification performance and error reduction. We further tested the hyperparameter tuning of our proposed approach with four attention layers and Leaky ReLU using epochs to determine which hyperparameters provide the best results. Table 5 and Figure 3 show that epoch 18 generated the best results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Convolution Layers** & **Activation Function** & **Attention Layers** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
4 & TANH & 4 & 83.23 & 80.12 & 80 & 83.12 \\ \hline
4 & Sigmoid & 4 & 84.12 & 83.23 & 82.12 & 80 \\ \hline
4 & RELU & 4 & 84.56 & 82.34 & 82.12 & 81.23 \\ \hline
4 & LEAKY RELU & 4 & 85.12 & 82.12 & 84.13 & 84.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different activation functions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN-Layer** & **Activation Function** & **Attention Layers** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
4 & Leaky Relu & 1 & 81.23 & 80.23 & 79.12 & 80 \\ \hline
4 & Leaky Relu & 2 & 85.12 & 82.12 & 84.13 & 84.12 \\ \hline
4 & Leaky Relu & 3 & 83.12 & 84.23 & 80.12 & 83.23 \\ \hline
4 & Leaky Relu & 4 & 86.12 & 84.23 & 85.23 & 85.12 \\ \hline
4 & Leaky Relu & 5 & 81.23 & 82.12 & 81.23 & 82.12 \\ \hline
4 & Leaky Relu & 6 & 80.12 & 82.12 & 80 & 79 \\ \hline
4 & Leaky Relu & 7 & 78.12 & 70.23 & 70 & 70 \\ \hline
4 & Leaky Relu & 8 & 80.12 & 80 & 79.12 & 76.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Leaky ReLU performance with different attention layers. In Table 6, we compare the proposed approaches with other machine learning and deep learning approaches, including the naive Bayes, logistic regression, and LSTM-RNN with the base hyperparameters, SVM, and random forest with the best-identified hyper-parameters. In comparison, the proposed deep learning approach was found to perform better than the other existing approaches, with an accuracy of 84.56%, precision of 82.34%, recall of 82.12%, and F-score value of 81.23%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
**Naive Bayes** & 67 & 68.12 & 69.23 & 68 \\ \hline
**Random Forest** & 60 & 61 & 62 & 60 \\ \hline
**SVM** & 62.13 & 63.12 & 62 & 63 \\ \hline
**Logistic Regression** & 70.12 & 69.123 & 70 & 67.12 \\ \hline
**LSTM-RNN** & 76.23 & 70.12 & 79.23 & 75.67 \\ \hline
**Proposed Approach** & 84.56 & 82.34 & 82.12 & 81.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the proposed approach in comparison to other existing approaches. Figure 3: Performance of the deep learning approach on epoch tuning. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epochs** & **Accuracy** & **Precision** & **Recall** & **Epochs** & **Accuracy** & **Precision** & **Recall** \\ \hline
1 & 80.12 & 80.23 & 80.23 & 11 & 82.34 & 81.73678 & 80.17906 \\ \hline
2 & 82.12 & 79.12 & 78.34 & 12 & 83.164 & 81.83814 & 80.49035 \\ \hline
3 & 81.23 & 80 & 80 & 13 & 83.1508 & 81.55977 & 80.38897 \\ \hline
4 & 81.34 & 81.23 & 78 & 14 & 83.15496 & 81.62612 & 80.15883 \\ \hline
5 & 82.34 & 80.45 & 81.2 & 15 & 83.18595 & 81.65663 & 80.18152 \\ \hline
6 & 83 & 81.23 & 82 & 16 & 82.99914 & 81.68349 & 80.21214 \\ \hline
7 & 83.23 & 83.23 & 80 & 17 & 83.13097 & 81.67283 & 80.25251 \\ \hline
8 & 83.13 & 81.228 & 79.96714 & 18 & 84.56 & 82.34 & 82.12 \\ \hline
9 & 83 & 81.4736 & 79.92959 & 19 & 83.40621 & 81.79581 & 80.54347 \\ \hline
10 & 84.12 & 81.52232 & 80.15668 & 20 & 83.45645 & 81.82975 & 80.55106 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Epoch tuning.