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Sentiment analysis of Arabic tweets in e-learning

Research Output: Contribution to journal Article Peer-review

Open access

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 553-563

Journal (Volume, Issue Number)

Journal of Computer Science (Volume 12, Issue 11)

Publication milestones

  • Published - 13/12/2016

Publication status

Published - 13/12/2016

ISSN

1549-3636

External Publication IDs

  • handle.net: 10547/623004
  • Scopus: 85013489188

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