  Due to the fast-growing volume of text documents and reviews in recent years,
current analyzing techniques are not competent enough to meet the users' needs.
Using feature selection techniques not only support to understand data better
but also lead to higher speed and also accuracy. In this article, the Whale
Optimization algorithm is considered and applied to the search for the optimum
subset of features. As known, F-measure is a metric based on precision and
recall that is very popular in comparing classifiers. For the evaluation and
comparison of the experimental results, PART, random tree, random forest, and
RBF network classification algorithms have been applied to the different number
of features. Experimental results show that the random forest has the best
accuracy on 500 features. Keywords: Feature selection, Whale Optimization
algorithm, Selecting optimal, Classification algorithm
