  The rise of social media such as blogs and social networks has fueled
interest in sentiment analysis. With the proliferation of reviews, ratings,
recommendations and other forms of online expression, online opinion has turned
into a kind of virtual currency for businesses looking to market their
products, identify new opportunities and manage their reputations, therefore
many are now looking to the field of sentiment analysis. In this paper, we
present a feature-based sentence level approach for Arabic sentiment analysis.
Our approach is using Arabic idioms/saying phrases lexicon as a key importance
for improving the detection of the sentiment polarity in Arabic sentences as
well as a number of novels and rich set of linguistically motivated features
contextual Intensifiers, contextual Shifter and negation handling), syntactic
features for conflicting phrases which enhance the sentiment classification
accuracy. Furthermore, we introduce an automatic expandable wide coverage
polarity lexicon of Arabic sentiment words. The lexicon is built with
gold-standard sentiment words as a seed which is manually collected and
annotated and it expands and detects the sentiment orientation automatically of
new sentiment words using synset aggregation technique and free online Arabic
lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and
Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product
reviews, etc.). The experimental results using our resources and techniques
with SVM classifier indicate high performance levels, with accuracies of over
95%.
