# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 4 Results  
In general, a total of 96 experiments were conducted. Tables 2 and 3 summarize the results obtained from all the experiments on MSA and DA, respectively. The outcomes of in-domain and no adaptation across all source-target pairs demonstrated a decline in performance caused by domain shifts, where the average accuracy became worse with DA (a drop from 83% to 65%) than with MSA (a drop from 85% to 78%). One possible explanation for this is that reviews are usually longer than tweets, resulting in a higher probability of domain overlap. The nature of dialects, on the other hand, had a significant impact, since individuals freely express their opinions in their personal and local tone without regard for linguistic standards, as discussed in Section 5. Moreover, all the models trained on TF/IDF representation showed varying degrees of negative transfers in some experiments (shown by the underlined accuracy results in Tables 2 and 3), where the obtained results for adaptation were lower than not performing adaptation at all. By contrast, AraBERT finetuning as well as other methods implemented on top of AraBERT successfully improved the results of the baseline (no adaptation) in all experiments. Interestingly, simply finetuning AraBERT yielded results that were competitive with AraBERT-ALDA and AraBERT-DANN, which illustrates the adaptable nature of pretrained LMs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Domains**} & \multicolumn{2}{c}{**In-Domain**} & \multicolumn{2}{c}{**No-Adapt**} & \multicolumn{2}{c}{**TF/IDF**} & \multicolumn{3}{c}{**AraBERT**} \\ \hline
**Source** & **Target** & **SVM** & **SVM** & **DANN** & **mSDA** & **DARL** & **Finetuning** & **ALDA** & **DANN** \\ \hline \multirow{2}{*}{HOT} & RES & 83.75 & 77.75 & 75.75 & **79.25** & **79.25** & **84.60** & 81.25 & 83.75 \\  & PRO & 81.00 & 73.00 & 70.75 & **74.25** & 69.00 & **81.25** & 80.75 & 78.75 \\ \hline \multirow{2}{*}{RES} & HOT & 89.50 & 85.50 & 78.00 & 83.00 & 74.75 & **92.38** & 92.25 & 91.75 \\  & PRO & 81.00 & 76.75 & 69.50 & **74.00** & 65.75 & 80.50 & **81.25** & 80.00 \\ \hline \multirow{2}{*}{PRO} & HOT & 89.50 & 83.25 & 71.75 & 77.75 & 76.50 & 87.00 & 86.25 & **92.00** \\  & RES & 83.75 & 71.50 & 62.50 & 66.25 & **76.75** & **83.16** & 76.00 & 81.75 \\ \hline \multicolumn{2}{c}{Average} & 84.75 & 77.96 & 71.38 & **75.75** & 73.67 & **84.82** & 82.96 & 84.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The classification accuracy results of in-domain, no adaptation baseline and domain adaptation methods on MSA (LAMD dataset).  
**Experiments on the MSA dataset:** Starting with experiments on TF/IDF features, mSDA outperformed other methods in most experiments, followed by DARL in two experiments. On the other hand, DANN failed to outperform baseline results, showing a negative transfer in all experiments. Nevertheless, when applied on top of AraBERT, both ALDA and DANN successfully adapted the classification to target domains. However, AraBERT finetuning achieved the highest average accuracy across all experiments.  
**Experiments on the DA dataset:** Again, DANN exhibited negative transfer in all experiments on TF/IDF features. In contrast, mSDA improved all baseline results and hence achieved the best average accuracy using TF/IDF features, followed by DALR. For AraBERT-based experiments, finetuning, ALDA and DANN all succeeded in improving baseline results in all experiments and achieved average accuracy that was even better than the average in-domain accuracy, with precedence of simple finetuning over DANN and ALDA.