# Exploiting Stacked Autoencoders for Improved Sentiment Analysis
## 5 Experimental Results and Evaluation
### Results
\begin{table}
\begin{tabular}{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\%) & 80.6 & 74.2 & 78.9 & 79.4 & 80.5 & 84.2 \\ Tweets Airline & 86.3 & 83.0 & 87.4 & 88.1 & 87.7 & 92.2 \\ Tweets SemEval & 84.1 & 80.1 & 86.4 & 88.3 & 85.0 & 90.1 \\ IMDb movie reviews (1) & 89.2 & 78.9 & 90.1 & 88.9 & 90.1 & 93.1 \\ IMDb movie reviews (2) & 86.9 & 82.9 & 89.6 & 87.6 & 89.3 & 89.6 \\ Cornell movie reviews 6 & 73.6 & 67.1 & 77.4 & 78.1 & 80.6 & 86.5 \\ Book reviews & 78.2 & 72.6 & 83.4 & 85.3 & 88.4 & 91.0 \\ Music reviews & 75.8 & 75.7 & 80.8 & 84.8 & 86.8 & 89.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Recall comparison 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\%) & 78.6 & 74.1 & 82.0 & 81.9 & 79.1 & 83.6 \\ Tweets Airline & 87.6 & 81.0 & 88.0 & 88.1 & 88.0 & 92.1 \\ Tweets SemEval & 82.2 & 81.1 & 83.6 & 78.3 & 84.1 & 92.0 \\ IMDb movie reviews (1) & 85.6 & 79.0 & 90.2 & 91.0 & 90.5 & 92.9 \\ IMDb movie reviews (2) & 87.9 & 82.7 & 89.3 & 91.5 & 89.6 & 89.2 \\ Cornell movie reviews 6 & 70.8 & 69.8 & 74.8 & 78.0 & 80.3 & 86.9 \\ Book reviews & 74.8 & 88.3 & 84.0 & 85.3 & 88.0 & 90.9 \\ Music reviews & 81.4 & 79.7 & 84.5 & 84.7 & 86.7 & 90.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Precision comparison for different types of datasets. To understand the significance of hyperparameter optimization, Table 7 compares the performance with and without hyperparameter optimization. We have used grid search, random search, and GA for the optimization of hyperparameters. For each dataset, we can observe that the baseline model without parameter optimization performs lower in terms of accuracy. In this research, grid search optimization performs better than random search support [55]. The performance of GA optimization surpasses baseline models. Another distinction that can be made is in regard to the relationship that exists between the solutions when the search is being carried out. In both the grid search and the random search, the majority of the results were zero. Because of the inherent independence of each answer, neither the grid search nor the random search offered the possibility of discovering any more patterns. There was no form of coordination amongst the various potential solutions in order to find the global optimal. On the other hand, GA(SAE)-SVM passed on the most successful solutions from one generation to the next. Because of this, the algorithm is able to gain knowledge from the previous generation and proceed with further exploration and exploitation of the search region. Ultimately, this strategy can assist the algorithm in producing answers that are very close to optimal. By analyzing the accuracy presented in Table 7, we observe that the proposed model achieved higher accuracy than the baseline for all datasets. Graphical representations of all the evaluation metrics are presented in Figure 4.  
Figure 4: Performance evaluation of all models. Figure 4a depicts accuracy comparison presented in Table 7, Figure 4b depicts precision comparison presented in Table 9, Figure 4c presents recall comparison presented in Table 8, and Figure 4d presents f1-score comparison presented in Table 10. By analyzing these results, our first research question is answered here. According to our evaluations of all optimization approaches, grid search took a long time compared to GA, and it also yielded poorer outcomes. There is a possibility that the hyperparameters that were manually specified were selected wrongly, which would lead to an inefficient model. Grid search optimization produces good results as compared to random search optimization. We discovered that random search is not a good choice for parameter optimization, as we could not detect much improvement in the results.