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Social media such as Twitter, Facebook, etc. has led to a generated growingnumber of comments that contains users opinions. Sentiment analysis researchdeals with these comments to extract opinions which are positive or negative.Arabic language is a rich morphological language; thus, classical techniques ofEnglish sentiment analysis cannot be used for Arabic. Word embedding techniquecan be considered as one of successful methods to gaping the morphologicalproblem of Arabic. Many works have been done for Arabic sentiment analysisbased on word embedding, but there is no study focused on variable parameters.This study will discuss three parameters (Window size, Dimension of vector andNegative Sample) for Arabic sentiment analysis using DBOW and DMPVarchitectures. A large corpus of previous works generated to learn wordrepresentations and extract features. Four binary classifiers (LogisticRegression, Decision Tree, Support Vector Machine and Naive Bayes) are used todetect sentiment. The performance of classifiers evaluated based on; Precision,Recall and F1-score.

Effect of Word Embedding Variable Parameters on Arabic Sentiment Analysis Performance