omcca
Opinion Mining Corpus for Colloquial Variety of Arabic language
Multiple institutions
Opinion Mining: Analysis of Comments Written
in Arabic Colloquial

dialect identification, sentiment analysis
World Congress on Engineering and Computer Science
Ahmed Y. Al-Obaidi,V. Samawi
In Arab nations, people used to express their
opinions using colloquial dialects depending on the country to
which they belong to. Analyzing reviews written in various
Arabic dialects is a challenging problem. This is because some
words could have different meanings in various dialects.
Furthermore, dialects could contain words that do not belong
to classical Arabic language.‎This research tackles the problem
of sentiment analysis of reviews and comments written in
colloquial dialects of Arabic language, at which the ability of
different machine learning algorithms and features are
examined in polarity determination. In this work, people's
reviews (written in different dialects) are classified into positive
or negative opinions. Each dialect comes with its own stopwords list. Consequently, a list of stop-words that suits
different dialects in addition to modern standard Arabic (MSA)
is suggested. In this paper, a light stemmer that suits dialects is
developed. Two feature sets are utilized (bag of words (BoW),
and N-gram of words) to investigate their effectiveness in
sentiment analysis. Finally, Naïve-Bayes, Support vector
machine (SVM), and Maximum Entropy machine learning
algorithms are applied to study their performance in opinion
mining. F1-measure is used to evaluate the performance of
these machine learning algorithms. To train and test the
suggested system performance, we built a corpus1
 of reviews by
collecting reviews written in two dialects (Saudi dialect and
Jordanian dialect). The testing results show that Maximum
Entropy outperforms the other two machine learning
algorithms. Using N-gram (with N=3) as features set improves
the performance of the three machine learning algorithms. 