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.

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This study investigates empirically the use of five different interactive menu conditions: adaptable, adaptive split, adaptive/adaptable highlighted, adaptive/adaptable minimised and mixed-initiative menus. The aim of the study is to compare the usability of these five menus, with regard to task accomplishment time and frequency of erroroccurrence. It also aims to examine the effects of different levels of adaptation and adaptability. In order to carry out this comparative investigation, five menus were built. These were then tested dependently using 30 subjects. Results show that overall the adaptable menu was surprisingly the best in terms of efficiency. Errors were also reduced in the adaptable menu by 50% when subjects customised their menus. Unexpectedly, subjects were slower using the adaptive split, mixed-initiative and minimised menus.

Sentiment analysis is utilised to assess users' feedback and comments. Recently, researchers have shown an increased interest in this topic due to the spread and expansion of social networks. Users' feedback and comments are written in unstructured formats, usually with informal language, which presents challenges for sentiment analysis. For the Arabic language, further challenges exist due to the complexity of the language and no sentiment lexicon is available. Therefore, labelling carried out by hand can lead to mislabelling and misclassification. Consequently, inaccurate classification creates the need to construct a relabelling process for Arabic documents to remove noise in labelling. The aim of this study is to improve the labelling process of the sentiment analysis. Two approaches were utilised. First, a neutral class was added to create a framework of reliable Twitter tweets with positive, negative, or neutral sentiments. The second approach was improving the labelling process by relabelling. In this study, the relabelling process applied to only seven random features (positive or negative): "earnings" ‫,)ارباح(‬ "losses" ‫,)خسائر(‬ "green colour" ‫,)باللون_االخضر(‬ "growing" ‫,)زياده(‬ "distribution" ‫,)توزيع(‬ "decrease" ‫,)اوخفاض(‬ "financial penalty" ‫,)غرامة(‬ and "delay" ‫.)تاجيل(‬ Of the 48 tweets documented and examined, 20 tweets were relabelled and the classification error was reduced by 1.34%.

Web usability is a significant factor in increasing user satisfaction, performance, trust, and loyalty. Web usability is particularly important for people who mostly depend on the website and for one reason or other cannot visit an institution, such as online distance education students. Accordingly, universities and educational websites need to determine the types of usability problems they have on their websites. However, far too little attention has been paid to providing detailed information regarding the types of specific usability problems that could be found on e-learning websites in general, and specifically, in the Kingdom of Saudi Arabia (KSA). The aim of this paper is to study and analyse the usability of university websites that offer distance education courses in the KSA. A total of 12 universities in Saudi Arabia were considered, which include 11 affiliated and one private university. The analysis of the data represents the level of usability of distance education websites. Results reveal that in Saudi Arabia, distance education websites are reliable, but violate basic usability guidelines.