While there has been a recent progress in the area of Arabic Senti- ment Analysis, most of the resources in this area are either of limited size, do- main specific or not publicly available. In this paper, we address this problem by generating large multi-domain datasets for Sentiment Analysis in Arabic. The datasets were scrapped from different reviewing websites and consist of a total of 33K annotated reviews for movies, hotels, restaurants and products. Moreover we build multi-domain lexicons from the generated datasets. Differ- ent experiments have been carried out to validate the usefulness of the datasets and the generated lexicons for the task of sentiment classification. From the ex- perimental results, we highlight some useful insights addressing: the best per- forming classifiers and feature representation methods, the effect of introducing lexicon based features and factors affecting the accuracy of sentiment classifi- cation in general. All the datasets, experiments code and results have been made publicly available for scientific purposes.
['Hady Elsahar', 'Samhaa R. El-Beltagy']
Building Large Arabic Multi-domain Resources for Sentiment Analysis