Sentiment Analysis of Arabic Tweets in e-Learning

Journal of computer sciences

AL-Rubaiee, Hamed¹ 

关键词: Sentiment Analysis;    Twitter;    Arabic Tweets;    Saudi Arabia;    King Abdulaziz University;    Machine Learning;    Pre-Processing;    e-Learning;   

DOI  :  10.3844/jcssp.2016.553.563

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

CC BY