Sentiment Analysis of Arabic Tweets in e-Learning
Authors:
- Hamed Al-RubaieeHAHamed Al-Rubaiee
- Renxi QiuRQRenxi Qiu
- Dayou LiDLDayou Li
Open Access
- 1 November 2016
- journal article
- Published by Science Publications in Journal of Computer Science
- Vol. 12 (11) , 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.Keywords
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