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Being popular in Saudi Arabia, we believe that tweets are a good source to capture the public\u2019s sentiment, especially since the country is in a fractious region. Going over the challenges and the difficulties that the Arabic tweets present \u2013 using Saudi Arabia as a basis \u2013 we propose our solution. A typical problem is the practice of tweeting in dialectical Arabic. Based on our observation we recommend a hybrid approach that combines semantic orientation and machine learning techniques. Through this approach, the lexical-based classifier will label the training data, a time-consuming task often prepared manually. The output of the lexical classifier will be used as training data for the SVM machine learning classifier. The experiments show that our hybrid approach improved the F-measure of the lexical classifier by 5.76% while the accuracy jumped by 16.41%, achieving an overall F-measure and accuracy of 84 and 84.01% respectively.<\/jats:p>","DOI":"10.1177\/0165551515610513","type":"journal-article","created":{"date-parts":[[2015,10,20]],"date-time":"2015-10-20T00:30:18Z","timestamp":1445301018000},"page":"782-797","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":106,"title":["Arabic tweets sentiment analysis \u2013 a hybrid scheme"],"prefix":"10.1177","volume":"42","author":[{"given":"Haifa K.","family":"Aldayel","sequence":"first","affiliation":[{"name":"King Saud University, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aqil M.","family":"Azmi","sequence":"additional","affiliation":[{"name":"King Saud University, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,7,11]]},"reference":[{"key":"bibr1-0165551515610513","unstructured":"Ahlqvist T, Back A, Halonen M, Heinonen S. 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