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Detecting the Online Shopping Factors Using the Arab Tweets on Media Technology

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With the popularization of the internet and media technology and the rapid adaption to the development of information technology, the online shopping has been accepted by millions of customers as a new model of shopping. It has been applied to different domains such as electronics, books and clothing marketing. Thus, it has led to the growth of hundreds of online shopping sites around the world and has created competitive electronic commerce. This paper analyzes the influencing factors on consumers of online shopping in the Arab world using Arabic tweets. Several classification algorithms have been used, such as support vector machine, naive Bayes, random forest, voting classifier and decision tree in Python and Weka. The results show that around 51% of users say that the online shopping is better than the traditional one based on the tweets in dataset. In addition, it has been found out that the random forest is the best algorithm in Python compared to other ones with an accuracy of 93.7% in 0.4 test size.
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Machine Learning; Online Shopping; Data Mining Tools; Hold-Out Method; Influencing Factors; Media Technology

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