@article{MAKHILLAJIT20191816748,
    title = {Arabic Semantic Classifier of Arabic Social Media "Twitter" Users},
    journal = {Asian Journal of Information Technology},
    volume = {18},
    number = {1},
    pages = {20-27},
    year = {2019},
    issn = {1682-3915},
    doi = {ajit.2019.20.27},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2019.20.27},
    author = {Jamal,Mohamad and},
    keywords = {Twitter,Tweet classification for Arabic text,data mining,Text analysis,spoken arabic dialects,comparison},
    abstract = {Text classification from text has gained a lot of interest in the last years, yet, some languages such
as Arabic (with its different spoken dialects) has not been given such attention. In this study, we present our
work in the text classification of Arabic texts with a focus on spoken Arabic dialects on Twitter messages.
Therefore, we have constructed a corpus of Arabic spoken Tweets and implemented two approaches to
automatically classify Tweet messages. One of the approaches uses different machine learning while the other
approach uses semantic web to do the classification. Further, a comparison has been made between the two
methods to determine the best configuration of the Tweets recognition system.}
    }