Research Article Open Access

Sentiment Analysis of Arabic Tweets in e-Learning

Hamed AL-Rubaiee1, Renxi Qiu1, Khalid Alomar2 and Dayou Li1
  • 1 University of Bedfordshire, United Kingdom
  • 2 King Abdulaziz University, Saudi Arabia

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.

Journal of Computer Science
Volume 12 No. 11, 2016, 553-563

DOI: https://doi.org/10.3844/jcssp.2016.553.563

Submitted On: 24 October 2016 Published On: 4 February 2017

How to Cite: AL-Rubaiee, H., Qiu, R., Alomar, K. & Li, D. (2016). Sentiment Analysis of Arabic Tweets in e-Learning. Journal of Computer Science, 12(11), 553-563. https://doi.org/10.3844/jcssp.2016.553.563

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Keywords

  • Sentiment Analysis
  • Twitter
  • Arabic Tweets
  • Saudi Arabia
  • King Abdulaziz University
  • Machine Learning
  • Pre-Processing
  • e-Learning