Abstract
Social networks have become one of our daily life activities not only in socializing but in e-commerce, e-learning, and politics. However, they have more effect on the youth generation all over the world, specifically in the Middle East. Arabic slang language is widely used on social networks more than classical Arabic since most of the users of social networks are young-mid age. However, Arabic slang language suffers from the new expressive (opinion) words and idioms as well as the unstructured format. Mining Arabic slang language requires efficient techniques to extract youth opinions on various issues, such as news websites. In this paper, we constructed a Slang Sentimental Words and Idioms Lexicon (SSWIL) of opinion words is built. In addition, we propose a Gaussian kernel SVM classifier for Arabic slang language to classify Arabic news comments on Facebook. To test the performance of the proposed classifier, several Facebook news comments are used, where 86.86% accuracy rate is obtained with precision 88.63 and recall 78.
Highlights
Sentiment Analysis of Arabic Slang Comments on FacebookSocial networks have become one of our daily life activities in socializing but in e-commerce, e-learning, and politics
The revolution of current social networks is affecting our every daily lives, having a vast amount of information through microblogs, review sites, web forums and online discussions
Most of the systems built for sentiment analysis are tailored for the English language [3] but there has been some work on other languages
Summary
Social networks have become one of our daily life activities in socializing but in e-commerce, e-learning, and politics. They have more effect on the youth generation all over the world, in the Middle East. Arabic slang language suffers from the new expressive (opinion) words and idioms as well as the unstructured format. Mining Arabic slang language requires efficient techniques to extract youth opinions on various issues, such as news websites. We propose a Gaussian kernel SVM classifier for Arabic slang language to classify Arabic news’ comments on Facebook. To test the performance of the proposed classifier, several Facebook news’ comments are used, where 86.86% accuracy rate is obtained with precision 88.63 and recall 78
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