## 6 Experimental Results
### Experiment D  
The fourth experiment was about assessing the influence of class imbalance on GBM, as well as on the other set of classifiers. For that reason, we considered both imbalanced sets, namely the C positive imbalanced and the D negative imbalanced set. In order to demonstrate that the proposed strategy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset B: Mixed** & **Accuracy** & **Precision (+)** & **Recall (+)** & **Precision (-)** & **Recall (-)** \\ \hline Decision Trees & 69.20\% & 59.68\% & 62.90\% & 75.88\% & 78.22\% \\ SVM & 76.58\% & 79.40\% & 68.30\% & 78.74\% & 86.90\% \\ Naive Bayes & 69.65\% & 58.58\% & 65.25\% & 76.75\% & 73.00\% \\ Deep Learning & 72.10\% & 68.12\% & 75.30\% & 82.60\% & 77.90\% \\ GBM & **83.90\%** & **82.10\%** & **78.15\%** & **86.47\%** & **88.20\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of GBM against other benchmarking methods for Dataset B: mixed. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline Decision Trees & Criterion: Gini Index; Maximal Depth: 40; Confidence: 0.25; Minimal Leaf Size: 20 \\ SVM & Kernel Type: polynomial; Degree: 2; C: 3.9; Epsilon: \(2\times 10^{-4}\) \\ Deep Learning & Activation: Rectifier; Hidden Layers: 2; Loss Function: Quadratic; Epochs: 20; \\ GBM & Loss Function: Ada-boost; Number of Trees: 100; Max-depth: 6; Learning rate: 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Parameter values, upon completion of the parameter tuning process for Experiment C. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline Decision Trees & Criterion: Gini Index; Maximal Depth: 50; Confidence: 0.3; Minimal Leaf Size: 30 \\ Naive Bayes & _No parameters to optimize_ \\ Deep Learning GBM & Activation: Techifier; Hidden Layers: 2; Loss Function: Quadratic; Epochs: 20; \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter values, upon completion of the parameter tuning process for Experiment B.  
of oversampling the minority class and under-sampling the majority is actually beneficial, we first tabulate the performance metric without using it and subsequently when including it. Table 8 arranges the outcomes of the experimental run on Dataset C positive imbalanced, without any handling of the class imbalance issue. Again, these outcomes were obtained when utilizing the PCA method, for 20% of the initial variance. An initial observation is that in this case, accuracy is actually not informative since it may appear high, but notice that the minority class label displays very poor results, meaning that almost all positive instances were incorrectly classified. Even though GBM again outperforms all other approaches, the 20% precision in the positive class is certainly not desirable. Thus, the following Figure 4 illustrates the results when considering the measures explained earlier, for dealing with class imbalance. The improvement of the positive class precision and recall is evident. From the initial range of 2% to 20% for the precision metric, upon applying the measures we discussed earlier, we can observe that GBM has reached almost 67.5% precision and 65% recall. The same behavior is also apparent for other classifiers, meaning that the measures paid off, since the minority class is not confronted with success. Table 9 tabulates the outcomes of the experimental run on Dataset D negative imbalanced, without any handling of the class imbalance issue. As seen from the above table, the minority class (which is now the negative sentiment) also suffers from deteriorated metrics, both in precision and recall. It is therefore evident that in real-world sentiment analysis tasks, where class imbalance is a common phenomenon, even the most sophisticated  
Figure 4: Evaluation scores of all classifiers against the positive imbalanced set upon taking measures for the class imbalance problem.