@article{MAKHILLJEAS2016111414072,
    title = {Sentiment Analysis in Arabic Social Media Using Association Rule Mining},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {11},
    number = {14},
    pages = {3239-3247},
    year = {2016},
    issn = {1816-949x},
    doi = {jeasci.2016.3239.3247},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2016.3239.3247},
    author = {Hai,Ahmed,Wafaa,Suryanti Binti,Mazlina Binti Abdul and},
    keywords = {machine learning,NLP,Arabic sentiment analysis,Association rule,feature selection method},
    abstract = {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).}
    }