Improving sentiment domain adaptation for Arabic using an unsupervised self-labeling framework

Yathrib Alqahtani, Nora Al-Twairesh, Ahmed Alsanad

Abstract: Highlights•An unsupervised self-labeling framework for Arabic sentiment domain adaptation.•Combining filter-based and embedded-based feature selections for pivots extraction.•A hybrid word similarity using co-occurrence association and embeddings similarity.•Evaluation on two multi-domain datasets: reviews in modern standard Arabic and tweets in dialectal Arabic.•A self-labeling domain adaptation is less sensitive to the sparsity and high dimensionality of Arabic texts than representation learning approach.