Data and Text Mining Techniques for Classifying Arabic Tweet Polarity
Belgacem Brahimi, Mohamed Touahria, Abdelkamel Tari
Journal of Digital Information Management, Vol. 14, No. 1, 2016.
DOI: 
Link: http://dline.info/fpaper/jdim/v14i1/v14i1_3.pdf
Abstract:
Sentiment analysis is a new task related
to text mining that extracts opinions from textual data
and classifies them into positive, negative or neutral.
The goal of this paper is to determine the effect of applying
stemming and n-gram techniques for Arabic texts
(tweets) on sentiment classification. This study also
aims at investigating the impact of feature selection on
the performance of the classifier. For this reason, three
classifiers Support Vector Machines (SVM), NaÃƒÂ¯ve
Bayes, (NB), and K-nearest neighbor (KNN) are used.
The obtained results showed that the best results of
performance are obtained when applying a hybrid representation
which includes tokens with character 3-
grams. The experiment results also revealed that the
use of feature selection technique improves significantly
the accuracy of the three classifiers for the task of
opinion classification. Regarding The classifiers, SVM
outperforms the other classifiers when using all the features,
while when selecting the most relevant features
by the SVM feature selection technique, SVM and NB
provided the best results.