3,322 research outputs found
Sentiment analysis of Arabic tweets in e-learning
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
Seminar Users in the Arabic Twitter Sphere
We introduce the notion of "seminar users", who are social media users
engaged in propaganda in support of a political entity. We develop a framework
that can identify such users with 84.4% precision and 76.1% recall. While our
dataset is from the Arab region, omitting language-specific features has only a
minor impact on classification performance, and thus, our approach could work
for detecting seminar users in other parts of the world and in other languages.
We further explored a controversial political topic to observe the prevalence
and potential potency of such users. In our case study, we found that 25% of
the users engaged in the topic are in fact seminar users and their tweets make
nearly a third of the on-topic tweets. Moreover, they are often successful in
affecting mainstream discourse with coordinated hashtag campaigns.Comment: to appear in SocInfo 201
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