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