## 4 Results
### Dataset-2: Tweet Emotion Dataset  
Our next set of experiments were performed on Tweet Emotion Dataset, the tagging process of which is explained in [16]. This dataset is a collection of labeled tweets in six emotion categories including joy, surprise, sadness, anger, fear and disgust. Table 11 shows class labels and number of tweets under each class label. It clearly indicates that the dataset is highly imbalanced, it has a high number of instances for the label 'Joy', whereas a very low number of instances for label 'Disgust'. In order to convert this imbalanced dataset to a balanced dataset, we generated enough number of instances for each class label to equal them to the number tweets for class label 'Joy' that is 8240 instances which is the class label with highest number of tweets. The corresponding balanced dataset number of instances is presented in Table 12.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Imbalanced Dataset**[15]} & \multicolumn{3}{c}{**Balanced Dataset**} \\ \hline
**Aspect** & **Precision** & **Recall** & **F1-Score** & **Precision** & **Recall** & **F1-Score** \\
**General** & 0.85 & 0.86 & 0.85 & 0.86 & 0.91 & 0.89 \\
**Teaching Skills** & 0.82 & 0.89 & 0.85 & 0.83 & 0.81 & 0.82 \\
**Knowledge** & 0.92 & 0.88 & 0.90 & 0.89 & 0.88 & 0.89 \\
**Behavior** & 0.89 & 0.80 & 0.84 & 0.89 & 0.88 & 0.88 \\
**Experience** & 0.80 & 0.81 & 0.80 & 0.93 & 0.91 & 0.92 \\
**Assessment** & 0.83 & 0.62 & 0.71 & 0.97 & 0.95 & 0.96 \\ \hline
**Overall** & **0.86** & **0.86** & **0.86** & **0.89** & **0.89** & **0.89** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Aspect Classification Results (Without Duplicates).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Aspect** & **Count** \\ \hline Teaching Skills & 1296 \\ Knowledge & 915 \\ General & 806 \\ Behavior & 669 \\ Experience & 239 \\ Assessment & 88 \\
**Sentiment** & **Count** \\
**Orientation** & 4250 \\ Positive & 1489 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Imbalanced Distribution [15] (Without Duplicates).  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Aspect** & **Count** \\ \hline Teaching Skills & 1296 \\ Knowledge & 1296 \\ General & 1296 \\ Behavior & 1296 \\ Experience & 1296 \\ Assessment & 1296 \\
**Sentiment** & **Count** \\
**Orientation** & 4250 \\ Negative & 1489 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Balanced Distribution (Without Duplicates).  
The classification model that we have used on Dataset-2 is summarized in Section 3, Figure 7. Table 13 shows accuracy achieved by the model of Figure 7 on both balanced and imbalanced dataset.  
The reason of reporting accuracy separately on six, two and four classes is that in previous work [25] the emotions were divided according to their polarity, that is joy and surprise considered positive polarities whereas sadness, anger, fear and disgust considered as negative polarity emotions.  
In all three cases, i.e., for six class labels, two and four, the accuracy improves with introducing balance to the dataset by generating text. For six class labels, the accuracy improves by 14% which is a significant improvement, where as it improves by 2.1% and 17.5% for two classes and fours classes respectively. The experiments on Tweet Emotion Dataset show that for different class labels, an accuracy improvement of ranging from 2.1% to 17.5% can be accomplished by balancing the dataset using text generation.