  With the social media engagement on the rise, the resulting data can be used
as a rich resource for analyzing and understanding different phenomena around
us. A sentiment analysis system employs these data to find the attitude of
social media users towards certain entities in a given document. In this paper
we propose a sentiment analysis method for Persian text using Convolutional
Neural Network (CNN), a feedforward Artificial Neural Network, that categorize
sentences into two and five classes (considering their intensity) by applying a
layer of convolution over input data through different filters. We evaluated
the method on three different datasets of Persian social media texts using Area
under Curve metric. The final results show the advantage of using CNN over
earlier attempts at developing traditional machine learning methods for Persian
texts sentiment classification especially for short texts.
