# Exploiting Stacked Autoencoders for Improved Sentiment Analysis
## 5 Experimental Results and Evaluation
### Results  
In this study, the GA(SAE)-SVM model is proposed for sentiment analysis. Hyperparameters learning rate, momentum, L2 regularization, and max epoch were optimized by GA. The GA helped to identify the best values of hyperparameters for each dataset. In the proposed hybrid model, four different models were trained to learn. At the end of the training period, the best success rate was obtained in the GA-SAE_Model4, with 70% training and 30% test data, using 20 neurons in the initial autoencoder and 10, 8, and 4 hidden neurons in the succeeding autoencoders, respectively. To ensure a fair comparison, simulations were performed in the same situation. Table 6 shows the results of four models. Parameters determined by GA include max epoch set to 150 and the Adam optimizer, with a learning rate of 0.001, momentum of 0.95, and L2 regularization set to 0.01. For each dataset, batch size was set to 32. SVM is used as a final classifier in this model. The output of GA(SAE) is fed into SVM and the final output is generated. Datasets are divided into training and test sets. Training sets are used to train the proposed model. Evaluation is performed using test sets. Accuracy, precision, recall, and F1-score are computed to compare the performance of the proposed model with the baseline models. The proposed model, GA(SAE)-SVM, provided better accuracy as compared to the baseline methods. Results of experiments are presented in Tables 7-10. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Models** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Sentiment140 (10\%) & Model 1 & 81.4 & 80.7 & 81.3 & 80.8 \\  & Model 2 & 82.2 & 81.4 & 82.5 & 81.6 \\  & Model 3 & 83.4 & 82.5 & 83.6 & 82.8 \\  & Model 4 & 84.5 & 83.6 & 84.2 & 83.9 \\ Tweets Airline & Model 1 & 89.9 & 88.9 & 89.4 & 88.3 \\  & Model 2 & 91.5 & 90.4 & 91.6 & 90.0 \\  & Model 3 & 92.2 & 91.3 & 92.8 & 91.8 \\  & Model 4 & 93.0 & 92.1 & 92.2 & 92.4 \\ Tweets SemEval & Model 1 & 90.3 & 89.4 & 90.3 & 88.9 \\  & Model 2 & 88.4 & 88.4 & 88.4 & 88.4 \\  & Model 3 & 90.9 & 90.8 & 90.3 & 90.3 \\  & Model 4 & 91.8 & 92.0 & 90.1 & 91.8 \\ IMDb movie reviews (1) & Model 1 & 91.2 & 90.4 & 90.3 & 91.2 \\  & Model 2 & 92.3 & 91.4 & 92.3 & 92.3 \\  & Model 3 & 90.3 & 90.3 & 89.8 & 89.9 \\  & Model 4 & 93.6 & 92.9 & 93.1 & 90.0 \\ IMDb movie reviews (2) & Model 1 & 87.3 & 87.3 & 87.4 & 87.3 \\  & Model 2 & 88.3 & 88.4 & 88.5 & 88.7 \\  & Model 3 & 89.2 & 89.3 & 89.8 & 89.8 \\  & Model 4 & 90.7 & 89.2 & 89.6 & 91.4 \\ Cornell movie reviews 6 & Model 1 & 85.3 & 85.8 & 85.8 & 85.9 \\  & Model 2 & 86.3 & 86.3 & 86.4 & 86.3 \\  & Model 3 & 85.4 & 85.4 & 85.4 & 85.3 \\  & Model 4 & 87.5 & 86.9 & 86.5 & 87.1 \\ Book reviews & Model 1 & 90.6 & 90.6 & 90.6 & 90.8 \\  & Model 2 & 88.8 & 87.4 & 88.6 & 87.3 \\  & Model 3 & 89.3 & 88.3 & 89.4 & 88.8 \\  & Model 4 & 91.0 & 90.9 & 91.0 & 91.0 \\ Music reviews & Model 1 & 87.0 & 86.4 & 87.3 & 87.2 \\  & Model 2 & 89.8 & 89.8 & 89.9 & 89.8 \\  & Model 3 & 90.3 & 89.3 & 90.3 & 89.4 \\  & Model 4 & 91.3 & 90.8 & 89.9 & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of four models for different types of datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **SAE** & **SVM** & **SAE-SVM** & **RS(SAE)-SVM** & **GS(SAE)-SVM** & **GA(SAE)-SVM** \\ \hline Sentiment140 (10\%) & 79.5 & 74.1 & 80.3 & 80.4 & 79.8 & 83.9 \\ Tweets Airline & 86.9 & 82.0 & 87.6 & 87.9 & 87.9 & 92.4 \\ Tweets SemEval & 83.1 & 80.6 & 84.9 & 82.8 & 81.5 & 91.8 \\ IMDb movie reviews (1) & 87.3 & 78.9 & 90.1 & 89.8 & 90.3 & 90.0 \\ IMDb movie reviews (2) & 87.4 & 82.7 & 89.4 & 89.5 & 89.5 & 91.4 \\ Cornell movie reviews 6 & 71.5 & 68.3 & 76.0 & 78.1 & 80.9 & 87.1 \\ Book reviews & 75.9 & 79.5 & 83.5 & 85.6 & 88.7 & 91.0 \\ Music reviews & 77.3 & 77.3 & 82.3 & 84.5 & 86.7 & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: F1-score comparison for different types of datasets.