| Autor: |
Mohammed Elbes, Wassan AL-dolime, Odai Sadaqa, Bilal Hawashin, Shadi AlZurbi, Hanadi Alshwabka, Tarek Kanan, Amal Aldajeh, Mohammad Ahmad Alia |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT). |
| DOI: |
10.1109/jeeit.2019.8717369 |
| Popis: |
Arabic Language is spoken widely in the world. It has very special characteristics that made it hard to be handled by computers. Recently, Social Media is considered as one of the richest source for knowledge sharing and information gathering in the internet. Arabic Natural Language Processing (ANLP) tools play major role when trying to understand the content of any Arabic textual data (e.g. social media), it helps clean noisy data, stem words, etc. Also, it assists with understanding of the semantic or sentiment contents. We use Arabic Machine Learning (Classification and Clustering) with social media to discover the polarity or opinion in the contents. Many kinds of classifiers and clusters used with Social Media content detection, like SVM and K-Mean. In this paper we review the literature of the popular ANLP tools with AML software on social media contents toward identifying the best tools in these domains. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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