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International Journal of Advanced Computer Science and Applications(ijacsa), Volume 10 Issue 2, 2019.
Abstract: Sentiment Analysis (SA) of social media contents has become one of the growing areas of research in data mining. SA provides the ability of text mining the public opinions of a subjective manner in real time. This paper proposes a SA model of Arabic Jordanian dialect tweets. Tweets are annotated on three different classes; positive, negative, and neutral. Support Vector Machines (SVM) and Naïve Bayes (NB) are used as supervised machine learning classification tools. Preprocessing of such tweets for SA is done via; cleaning noisy tweets, normalization, tokenization, namely, Entity Recognition, removing stop words, and stemming. The results of the experiments conducted on this model showed encouraging outcomes when Arabic light stemmer/segment is applied on Arabic Jordanian dialect tweets. Also, the results showed that SVM has better performance than NB on such tweets’ classifications.
Jalal Omer Atoum and Mais Nouman, “Sentiment Analysis of Arabic Jordanian Dialect Tweets” International Journal of Advanced Computer Science and Applications(ijacsa), 10(2), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100234
@article{Atoum2019,
title = {Sentiment Analysis of Arabic Jordanian Dialect Tweets},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100234},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100234},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {2},
author = {Jalal Omer Atoum and Mais Nouman}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.