# PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource Languages
## 4 Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & \begin{tabular}{l} **Sentiment** \\ **F1 (\%)** \\ \end{tabular} & \begin{tabular}{l} **Irony** \\ **F1 (\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Hate Speech** \\ **F1 (\%)** \\ \end{tabular} &
\begin{tabular}{l} **Emotion** \\ **F1 (\%)** \\ \end{tabular} \\ \hline Naive Bayes & 38.18 & 48.56 & 36.20 & 20.30 \\ Decision Tree & 47.83 & 70.70 & 55.71 & 56.44 \\ Random Forest & 46.51 & 82.25 & 49.81 & 76.20 \\ Logistic Regression & 45.25 & 69.23 & 49.81 & 29.05 \\ Support Vector Machine & 27.50 & 48.11 & 49.81 & 12.76 \\ \hline Greek BERT + MLP & 59.25 & 78.8 & 98.62 & 81.76 \\ Greek BERT + Enc & 67.76 & 93.17 & 99.54 & 81.99 \\ \hline Sentiment (IHES) & 67.76 & - & - & - \\ Irony (ESHI) & - & 93.17 & - & - \\ Hate speech (ISEH) & - & - & 99.54 & - \\ Emotion (IHES) & - & - & - & 82.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Baseline experimental results \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Models** & \begin{tabular}{l} **Sentiment** \\ **F1 (\%)** \\ \end{tabular} & \begin{tabular}{l} **Irony** \\ **F1 (\%)** \\ \end{tabular} & \
\begin{tabular}{l} **Hate Speech** \\ **F1 (\%)** \\ \end{tabular} &
\begin{tabular}{l} **Emotion** \\ **F1 (\%)** \\ \end{tabular} \\ \hline PIMA & & & & \\ - Greek BERT & 64.1 & 93.1 & 99.54 & 81.3 \\ - DAGB & 65.2 & 93.8 & 99.54 & 82.18 \\ \hline PIMA /w LMP & & & & \\ - Greek BERT & 69.4 & 92.5 & 99.4 & 82.4 \\ - DAGB & 70.2 & 92.5 & 99.4 & 93.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Proposed model experimental result training yields a better overall performance, which is improved further with the use of our domain-adapted Greek BERT. In order to further understand the experimental results, we have to take into account the label distribution in the dataset (Section 3.2). While we have made efforts to balance the data through sampling techniques (Section 4.2) which ensures scarce class examples are included, having parallel data makes it difficult to completely balance all classes. Based on this imbalance, the contribution of each class to the model's optimization needs to be adjusted, hence the performance increase in LMP. It is also due to this imbalance in the training data that sentiment experiences a performance drop when all targets are considered equally. While methods do exist to artificially generate examples [53], they work on a single class setting and would not work in our parallel corpus. By having a parallel corpus, we were also able to effectively investigate the effects of parallel training in these four aspects of subjectivity. Our results indicate that a suspected connection between these targets exists, while the parameter sharing layer results in a performance increase compared to individual models. In two out of the three combination trained models, emotion is the final subjectivity before the target class. This result suggests a strong connection between identified emotions and both sentiment and hate speech. This is in line with findings in psychology, which highlight that the basic emotions which we use in this study are clearly separated [45]. Hence, we can deduce that clear connections between sets of emotions with different weights are directly related to sentiment and hate speech. For emotion on the other hand, the best performance is achieved with sentiment as the most recent subjectivity target. In comparison to irony and hate speech, emotion is expressed in more classes, making it easier to transfer to the six classes of emotion. It is also extremely important to note that, by definition, imbalance in the data will exist and methodologies to handle this imbalance are required in order to exploit these implicit connections. The advantages of predicting all four targets with a single forward pass is also very important in real-world deployment of such models. Not only does it save time and computational resources, but it has the potential to increase the throughput of the system.