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DOI: 10.14569/IJACSA.2019.0100234
PDF

Sentiment Analysis of Arabic Jordanian Dialect Tweets

Author 1: Jalal Omer Atoum
Author 2: Mais Nouman

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 2, 2019.

  • Abstract and Keywords
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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.

Keywords: Sentiment analysis; Arabic Jordanian dialect; tweets; machine learning; text mining

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.

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