Sentiment analysis refers to the task of identifying polarity of positive and negative for particular text that yield an opinion. Arabic language has been expanded dramatically in the last decade especially with the emergence of social websites (e.g. Twitter, Facebook, etc.). Several studies addressed sentiment analysis for Arabic language using various techniques. The most efficient techniques according to the literature were the machine learning due to their capabilities to build a training model. Yet, there is still issues facing the Arabic sentiment analysis using machine learning techniques. Such issues are related to employing robust features that have the ability to discriminate the polarity of sentiments. This paper proposes a hybrid method of linguistic and statistical features along with classification methods for Arabic sentiment analysis. Linguistic features contains stemming and POS tagging, while statistical contains the TF-IDF. A benchmark dataset of Arabic tweets have been used in the experiments. In addition, three classifiers have been utilized including SVM, KNN and ME. Results showed that SVM has outperformed the other classifiers by obtaining an f-score of 72.15%. This indicates the usefulness of using SVM with the proposed hybrid features.
الخلاصة
يجمع المختصون أن ليس من نقد إلا وكان مُرحِّلا لنظرية لسانية إلى فضائه، فكان أثر اللسانيات السوسيرية، ومن ثم اللسانيات التوليدية التحويلية معروفا، ولم يكن هذا الأثر محدودا باللسانيين أو نقاد الأدب فحسب، بل امتد إلى الشعراء والجماليين أيضا. وفي هذا البحث سنتوقف عند أثر اللسانيات الوظيفية، وهو ما لم يتوقف عنده إلا قلة من المختصين. ولن نقف عند نقد الوظيفيين للشعر، بل سنقف عند نقدهم للرواية، بقراءة
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This study seeks to examine the language context of the prominent Revolution’s poet, Moufdi Zakaria, in his anthology “ The Algerian Odyssey " )إلياذة الجزائر( in which he makes use of the Coranic terms explicitly and implicitly. Besides, it seeks to explore the content of his terms from a lexicographic and semantic point of view in their Islamic and human dimensions
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