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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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Product reviews are becoming increasingly useful. In this paper, Twitter has been chosen as a platform for opinion mining in trading strategy with Mubasher products, which is a leading stock analysis software provider in the Gulf region. This experiment proposes a model for sentiment analysis of Saudi Arabic (standard and Arabian Gulf dialect) tweets to extract feedback from Mubasher products. A hybrid of natural language processing and machine learning approaches on building models are used to classify tweets according to their sentiment polarity into one of the classes positive, negative and neutral. In addition, Regarding to the comparison between SVM and Bayesian method, we have split the data into two independents subsets form different periods and the experiments were carried out for each subsets respectively in order to distinction between positive and negative examples by using neutral training examples in learning facilitates. Similar result has been given.

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%.