Sentiment Analysis of Arabic Tweets Using SVM Classifier with POS Tagging Features

Kamel Jafar, Panov Alexander

Abstract


Social media platforms are open spaces that allow their users to express their opinions freely, which made it one of the most popular and widely used Internet sites, including Twitter, which is among the most visited social networking sites, as the number of its users' increases day by day. Due to the amount of information, opinions, and points of view that these sites contain, the importance of analyzing and extracting these opinions and benefiting from them in various fields, to allow the beneficiaries of this information to take appropriate decisions according to the result of analyzing the texts written in them and classifying them according to certain classifications. The field of opinion mining and sentiment analysis has received great attention from researchers, but most studies have focused on English texts. Therefore, in this research, Arabic texts were studied in this field, especially after the increased demand for sentiment analysis tools for Arabic texts written in standard and colloquial. The research relied on machine learning technology and used the Support Machine Vector algorithm to classify tweets into tweets with positive, negative, or neutral fingerprints because it is one of the good algorithms for classifying texts in general.


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