Journal of Computer Science

Sentiment Analysis of Arabic Tweets in e-Learning

Hamed AL-Rubaiee, Renxi Qiu, Khalid Alomar and Dayou Li

DOI : 10.3844/jcssp.2016.553.563

Journal of Computer Science

Volume 12, Issue 11

Pages 553-563

Abstract

In this study, we present the design and implementation of Arabic text classification in regard to university students' opinions through different algorithms such as Support Vector Machine (SVM) and Naive Bayes (NB). The aim of the study is to develop a framework to analyse Twitter "tweets" as having negative, positive or neutral sentiments in education or, in other words, to illustrate the relationship between the sentiments conveyed in Arabic tweets and the students' learning experiences at universities. Two experiments were carried out, one using negative and positive classes only and the other one with a neutral class. The results show that in Arabic, a sentiments SVM with an n-gram feature achieved higher accuracy than NB both with using negative and positive classes only and with the neutral class.

Copyright

© 2016 Hamed AL-Rubaiee, Renxi Qiu, Khalid Alomar and Dayou Li. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.