# Affect Analysis in Arabic Text: Further Pre-Training Language Models for Sentiment and Emotion
## 5 Results and Discussion  
Exp-I: Experiment to Investigate the Influence of the Within-Task Adaptation Approach on Sentiment and Emotion Classification Performance  
The results of fine-tuning the BERT-base-QaRiB, QST, QSR, and QSRT models on the SS2030, Twitter-AB, 40k-Tweets, ArSentiment, and MASC datasets are shown in Table 10. Table 11 presents the results obtained by fine-tuning the BERT-base-QaRiB, QE3, and QE6 models using the EATD and ExaAEC datasets. The results are discussed, compared, and analysed specifically based on the macro-F1 score for the partition of the test set. In addition, the results of each dataset of the base studies [75, 76, 77, 78, 79, 80] are presented in Tables 10 and 11. The results that showed an enhancement above the results obtained by the baseline model (i.e., the BERT-base-QaRiB model) are typically highlighted in bold. The results that are highlighted in bold and underlined are the best results that have been achieved for each dataset according to the model used. In total, 26 separate experiments were carried out utilising various sentiment and emotion datasets. In Table 10, the results that outperformed the BERT-base-QaRiB model results are highlighted in bold. All sentiment models, including QST, QSR, and QSRT, outperformed the BERT-base-QaRiB model on all sentiment datasets. In addition, when comparing the QST model to the QSR model across all of the experiments (given in Table 10), we observed that the sentiment datasets that were sourced from Twitter, including SS2030, Twitter-AB, and 40k-Tweets, the QST model outperformed the QSR model. Based on this, we may infer \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Datasets** & **Models** & **Precision** & **Recall** & **Accuracy** & **Macro-F1** \\ \hline \multirow{8}{*}{**EATD**} & **SVM**[75] & 90.1\% & - & 89.83\% & 89.7\% \\  & **BERT-base-QaRiB** & 90.87\% & 90.56\% & 91.06\% & 90.70\% \\  & **QS2030** & 92.97\% & 92.89\% & 93.18\% & **92.93\%** \\  & **QSR** & 91.35\% & 91.70\% & 91.76\% & **91.51\%** \\  & **QSRT** & 92.05\% & 92.47\% & 92.47\% & **92.24\%** \\ \cline{2-6} \multirow{4}{*}{**Twitter**} & **SVM**[77] & - & - & 87.2\% & - \\  & **BERT-base-QaRiB** & 94.47\% & 94.32\% & 94.39\% & 94.37\% \\  & **Twitter-AB** & **QST** & 96.93\% & 96.93\% & 96.94\% & **96.93\%** \\  & **QSR** & 96.41\% & 96.49\% & 96.43\% & **96.43\%** \\  & **QSRT** & 97.43\% & 97.47\% & 97.45\% & **97.45\%** \\ \cline{2-6} \multirow{4}{*}{**Twitter**} & **LSTM**[76] & 86.94\% & 88.9\% & 88.05\% & 87.24\% \\  & **BERT-base-QaRiB** & 90.56\% & 90.51\% & 90.50\% & 90.49\% \\  & **QST** & 91.38\% & 91.37\% & 91.37\% & **91.37\%** \\  & **QSR** & 91.34\% & 91.27\% & 91.27\% & **91.27\%** \\  & **QSRT** & 90.66\% & 90.65\% & 90.65\% & **90.65\%** \\ \hline \multirow{8}{*}{**Reviews**} & **SVM**[78] & - & - & 59.9\% & - \\  & **BERT-base-QaRiB** & 81.21\% & 72.52\% & 90.62\% & 75.93\% \\  & **QST** & 79.04\% & 75.34\% & 90.48\% & **76.83\%** \\  & **QSR** & 81.61\% & 76.53\% & 91.45\% & **78.53\%** \\  & **QSRT** & 81.20\% & 75.83\% & 91.25\% & **78.15\%** \\ \cline{2-6} \multirow{4}{*}{**Reviews**} & **LLR**[79] & - & - & - & 97.8\% \\  & **BERT-base-QaRiB** & 95.65\% & 93.81\% & 95.10\% & 94.61\% \\  & **QST** & 97.04\% & 97.20\% & 97.33\% & **97.12\%** \\  & **QSR** & 95.95\% & 95.37\% & 95.99\% & **95.65\%** \\  & **QSRT** & 96.14\% & 96.53\% & 96.58\% & **96.33\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results of fine-tuning emotion models on emotion datasets (All-metrics). that data distributions of tasks within the same source or domain could be similar. This also indicates that further pre-training of the model using task-specific datasets from the same genre or domain of the fine-tuning datasets yields better results than utilising datasets from a different genre or domain. Compared to the BERT-base-QaRiB model, Table 10 reveals a performance gain of 2.22% for the QST model and 0.80% for the QSR model on the SS2030 dataset.