# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 5 Discussion
### MSA vs. DA  
The two sets of experiments on MSA and DA shared a few similar observations. First, DANN suffered a negative transfer when applied on TF/IDF features, but it achieved far better results for accuracy when applied on top of AraBERT. Second, all AraBERT-based experiments were successfully improved over the baseline accuracy results, and simply finetuning AraBERT showed the best average accuracy. Third, mSDA outperformed all other methods on TF/IDF representation. In contrast, a distinguishable observation between the two sets of experiments is that mSDA totally succeeded in avoiding negative transfer in the experiments on DA, but not on MSA, where it showed many negative transfer cases.  
Moving to another evaluation metric, Figure 3 illustrates the average transfer loss of each method in the two sets of experiments calculated using Equation (1). The results generally show that the adaptation performance on MSA was better than on DA (i.e., lower average transfer loss), except for AraBERT, AraBERT-ALDA and AraBERT-DANN, which performed even better than the in-domain SVM classifier on DA, as illustrated by the negative values of average transfer loss. In line with the accuracy results, finetuning AraBERT is the only method that avoided the transfer loss in both datasets.  
Although different types of text were used in the two datasets (i.e., reviews vs. tweets), this was expected, due to the extra challenges imposed by DA. Regardless of the domains, DA involves a wide range of dialects spoken across the Arab world. People tend to express their opinions and feelings in their local dialects. In contrast to MSA, no rules  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Domains**} & \multicolumn{1}{c}{**In-Domain**} & \multicolumn{1}{c}{**No-Adapt**} & \multicolumn{3}{c}{**TF/IDF**} & \multicolumn{3}{c}{**AraBERT**} \\ \hline
**Source** & **Target** & **SVM** & **SVM** & **DANN** & **mSDA** & **DARL** & **Finetuning** & **ALDA** & **DANN** \\ \hline \multirow{2}{*}{POL} & SOC & 76.25 & 67.25 & 64.75 & **71.50** & 71.25 & **86.00** & 83.50 & 85.25 \\  & SPO & 87.25 & 61.00 & 55.75 & **61.25** & 55.25 & **89.40** & 80.75 & 78.25 \\ \hline \multirow{2}{*}{SOC} & POL & 85.75 & 72.50 & 60.00 & 73.50 & **80.75** & 90.25 & **90.75** & **90.75** \\  & SPO & 87.25 & 67.00 & 50.00 & **70.00** & 68.25 & 85.50 & 82.00 & **88.50** \\ \hline \multirow{2}{*}{SPO} & POL & 85.75 & 57.50 & 51.50 & **72.25** & 60.00 & **90.60** & 85.75 & 90.25 \\  & SOC & 76.25 & 63.00 & 56.50 & **65.00** & 61.75 & 82.20 & **85.00** & 81.75 \\ \hline \multicolumn{2}{c}{Average} & 83.08 & 64.71 & 56.42 & **68.92** & 66.21 & **87.33** & 84.63 & 85.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The classification accuracy results of in-domain, no adaptation baseline and domain adaptation methods on DA (MARSA dataset).  
or standards can be set for DA, making them subject to change over time, during which pronunciation changes, new words are coined or borrowed and the meaning of old terms may shift or invert. As a result, more divergence and feature mismatches are expected. This demonstrates the need for additional effort when working on domain adaptation for DA sentiment classification. However, the adaptation results of transformer-based LMs in handling DA were promising, which should encourage the creation of larger LMs that cover more dialects.