# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 2 Literature Review  
Domain adaptation has been widely used in fields such as computer vision and NLP to take advantage of the remarkable progress that has been made in supervised learning,while simultaneously overcoming the problem of limited labeled data in some domains. In NLP, domain adaptation facilitates a range of tasks, including sentiment classification, part-of-speech (POS) tagging and parsing [5]. Although most proposed adaptation methods are task-independent, some are sentiment-specific. Domain adaptation generally follows either feature-based or instance-based adaptation or a combination of the two. Feature-based approaches address feature mismatch by attempting to learn a shared feature space between the source and the target domain [14]. Alternatively, some target features can be added to the source domain through feature augmentation. A common concept implemented in these methods is the pivot, which refers to any shared features of the task in both domains [4]. Methodologies in this line include projection learning, feature expansion, and deep learning. Initial forms of projection learning were structural correspondence learning (SCL) [4] and spectral feature alignment [15], which aimed to bridge the differences in distribution between source and target features by learning a domain-invariant shared space. To accomplish the same goal, feature expansion augments source features with borrowed features from the target domain. One method of feature expansion has been implemented using joint sentiment/topic [16] by augmenting polarity-bearing topics extracted from the target domain into the source domain's feature space [17]. Another method used the sentiment sensitive thesaurus [18], which selects adjectives, adverbs and nouns labeled with their corresponding labels. Their relations to all lexical terms are then assigned using pointwise mutual information to extend each source document with the top related terms. Additionally, a recently applied feature expansion method uses core/periphery decomposition on a weighted graph structure [19]. Under the feature-based deep learning approach, there are autoencoder-based methods and pivot-based methods [5]. Denoising autoencoders (DA) apply representation learning by training networks consisting of an encoder and a decoder to minimize the reconstruction loss after corrupting the original text with noise, typically by scrambling or masking words [20]. Stacked denoising autoencoders (SDA) use multiple DAs to create an invariant feature space with higher-level representation [21]. Though effective, SDA suffers from high computational costs and a limited ability to work with higher-dimensional data. This problem was later addressed through mSDA [11], which considers domain-independent features during reconstructions. Interestingly, the first pivot-based deep learning method combined autoencoders and SCL [22], where non-pivot features were encoded into low-dimensional representations to learn how to predict and reconstruct pivot features. Later, pivot-based language modeling [23] combined SCL with a language model (LM) to predict the presence of pivots instead of following words, leading to context-aware representations. Without needing a pivot selection step, a hierarchical attention transfer network [24] can automatically distinguish between pivots and non-pivots through two hierarchical attention networks, in which a classifier is trained on the shared hidden representations. Instance-based approaches work on the instance level to reduce domain discrepancy, rather than directly on features. These approaches are domain (or instance) selection, self-labeling and deep learning. The selection methods assign a weight to each source (or instance) based on its similarity to the target domain in such a way that the distance between the source and target domains is minimized [25]. Sometimes, such an approach is used as preparation for the adaptation process [26].