Abstract
The increased number of Arab users on microblogging services who use Arabic language to write and read has triggered several researchers to study the posted data and discover the user’s opinion and feelings to support decision making. In this paper, a sentiment analysis framework is presented for slang Arabic text. A new dataset with Jordanian dialect is presented. Numerous specific Arabic features are shown with their impact on slang Arabic Tweets. The new set of features consists of lexicon, writing style, grammatical and emotional features. Several experiments are conducted to test the performance of the proposed scheme. The new proposed scheme produces better results in comparison with others. The experiments show that the system performs well without translating the tweets to English or standard Arabic.
Highlights
Social media has become a powerful source of information and part of our daily life
EXPERIMENTAL RESULTS Several experiments were conducted to test the performance of the proposed scheme
We demonstrated the potential and the much-improved performance of the proposed technique
Summary
Social media with huge volume of data attract more people every day from different cultures, societies and languages. These data could be analyzed to capture valuable information about various topics. Sentiment analysis for English text has been researched heavily and several public datasets have been created and made publicly available. SA for Arabic language has not been researched seriously due to the language nature It has many difficulties including the complexity of Arabic grammars, multi-meaning of a single word (ex: "َِٜ اىٞ "ػThe word " ِٞ "ػmeans eye and the right meaning in this clause is water source), multi-accents different meaning
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