Though much research has been conducted on Subjectivity and Sentiment Analysis (SSA) during the last decade, little work has focused on Arabic. In this work, we focus on SSA for both Modern Standard Arabic (MSA) news articles and dialectal Arabic microblogs from Twitter. We showcase some of the challenges associated with SSA on microblogs. We adopted a random graph walk approach to extend the Arabic SSA lexicon using ArabicEnglish phrase tables, leading to improvements for SSA on Arabic microblogs. We used different features for both subjectivity and sentiment classification including stemming, part-of-speech tagging, as well as tweet specific features. Our classification features yield results that surpass Arabic SSA results in the literature.
Mourad and Darwish REF present in their work another way for (SSA) subjectivity and sentiment analysis on Arabic news articles and dialectal Arabic microblogs from twitter.
Subjectivity and Sentiment Analysis of Modern Standard Arabic and Arabic Microblogs
WASSA
Computer Science