# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
###### Abstract  
Over the last decade, many methods have been developed to address the domain dependency problem of sentiment classification under domain shift. This problem is exacerbated in Arabic by its feature sparsity induced by morphological complexity and dialect variability. However, only a few studies have proposed sentiment domain adaptation methods for Arabic, with inconsistent comparisons resulting from different datasets and settings, making it difficult to identify the most effective approaches. This is the first comparative study of the most effective domain adaptation methods for Arabic sentiment classification. We replicate the existing methods proposed for Arabic and compare their effectiveness on the standard dataset settings. To further examine the extent to which adaptation performance differs between Modern Standard Arabic (MSA) and Dialectal Arabic (DA), we employ two public multi-domain sentiment datasets. We also test two well-established methods that have been thoroughly utilized in English-related studies and examine if they maintain the same levels of performance when applied to Arabic. Our findings indicate that adaptation performance on MSA is better than on DA for all traditional approaches. However, implementing adaptation on top of transformer-based language models shows superior performance on DA. Finally, methods that have proven to excel in English suffer from low performance when applied to Arabic and exhibit negative transfer in most cases. sentiment classification; domain adaptation; Arabic language +
Footnote †: journal:  
In other words, people convey their feelings in various ways across domains using different words [4]. As illustrated by Figure 1, the word \"delicious\" conveys a positive view in the restaurants domain but not in the products domain. Machine learning assumes that training and test sets are drawn from the same underlying distribution. Therefore, a classifier trained on product reviews, for instance, will experience a drop in performance when applied to restaurant reviews due to what is called a domain shift or feature mismatch [5]. By using labeled product reviews and available restaurant reviews (unlabeled), adaptation methods can attempt to train a satisfactory classifier for the restaurants domain. Formally, given that \(\text{D}_{\text{S}}=\{\text{X}_{\text{S}},\text{P}(\text{X}_{\text{S}})\}\) and \(\text{D}_{\text{T}}=\{\text{X}_{\text{T}},\text{P}(\text{X}_{\text{T}})\}\) are source and target domains, respectively; \(\text{X}_{\text{S}}\) and \(\text{X}_{\text{T}}\) are the source and target feature spaces, respectively; \(\text{P}(\text{X}_{\text{S}})\) and \(\text{P}(\text{X}_{\text{T}})\) are probability distributions over \(\text{X}_{\text{S}}\) and \(\text{X}_{\text{T}}\) such that \(\text{P}(\text{X}_{\text{S}})\
eq\text{P}(\text{X}_{\text{T}})\); \(\text{T}_{\text{S}}=\{\text{Y}_{\text{S}},\text{P}_{\text{S}}(\text{Y}_{ \text{S}}\,\text{1}\,\text{X}_{\text{S}})\}\) and \(\text{T}_{\text{T}}=\{\text{Y}_{\text{T}},\text{P}_{\text{T}}(\text{Y}_{ \text{T}}\text{X}_{\text{T}})\}\) are the source and target tasks, respectively; \(\text{Y}_{\text{S}}\) and \(\text{Y}_{\text{T}}\) are the source and target label spaces, respectively, such that \(\text{Y}_{\text{S}}=\text{Y}_{\text{T}}\); \(\text{P}_{\text{S}}(\text{Y}_{\text{S}}\,\text{1}\,\text{X}_{\text{S}})\) is the source conditional probability distribution; and \(\text{S}_{\text{L}}\) and \(\text{T}_{\text{UL}}\) are sufficiently labeled data in \(\text{D}_{\text{S}}\) and unlabeled data in \(\text{D}_{\text{T}}\), respectively. Unsupervised domain adaptation then uses \(\text{S}_{\text{L}}\) and \(\text{T}_{\text{UL}}\) to learn \(\text{P}_{\text{T}}(\text{Y}_{\text{T}}\text{X}_{\text{T}})\), the conditional probability distribution of the target domain. Throughout this study, we use the term \"domain adaptation\" rather than \"unsupervised domain adaptation.\" Figure 2 describes the underlying idea behind domain adaptation. Figure 1: Examples of different words from two different domains.