Polarity Classification of Arabic Sentiments

Mohammed N. Al-Kabi, Heider A. Wahsheh and Izzat M. Alsmadi
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Mohammed N. Al-Kabi: Information Technology Faculty, Zarqa University, Zarqa, Jordan
Heider A. Wahsheh: College of Computer Science, King Khaled University, Abha, Saudi Arabia
Izzat M. Alsmadi: University of New Haven, West Haven, CT, USA

International Journal of Information Technology and Web Engineering (IJITWE), 2016, vol. 11, issue 3, 32-49

Abstract: Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Date: 2016
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