Name: omcca
Description: Opinion Mining Corpus for Colloquial Variety of Arabic language
Provider: Multiple institutions
Paper Title: Opinion Mining: Analysis of Comments Written
  in Arabic Colloquial
  
Tasks: dialect identification, sentiment analysis
Venue Name: World Congress on Engineering and Computer Science
Authors: Ahmed Y. Al-Obaidi,V. Samawi
Abstract: 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. 