TY  - JOUR
T1  - Sentiment Analysis in Arabic Social Media Using Association Rule Mining
AU - Tao, Hai AU - AL-Saffar, Ahmed AU - ALSaiagh, Wafaa AU - Awang, Suryanti Binti AU - Majid, Mazlina Binti Abdul AU - Sabri, Bilal 
JO  - Journal of Engineering and Applied Sciences
VL  - 11
IS  - 14
SP  - 3239
EP  - 3247
PY  - 2016
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2016.3239.3247
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2016.3239.3247
KW  - machine learning
KW  -NLP
KW  -Arabic sentiment analysis
KW  -Association rule
KW  -feature selection method
AB  - The fast-paced growth in worldwide webs has resulted in the development of sentiment analysis it
involves the analysis of comments or web reviews. The sentiment classification of the Arabic social media is
an exciting and fascinating area of study. Hence this study brings forth a new method engaging association
rules with three Feature Selection (FS) methods in the Sentiment Analysis (SA) of web reviews in the Arabic
language. The feature selection methods used are (&#967;<sup>2</sup>), Gini Index (GI) and Information Gain (GI). This study
reveals that the use of feature selection methods has enhanced the classifier results. This means that the
proposed model shows a better result than the baseline result. Finally, the experimental results show that the
Chi-square Feature Selection (FS) produces the best classification technique with a high accuracy of f-measure
(86.811).
ER  -