  The rise of social media is enabling people to freely express their opinions
about products and services. The aim of sentiment analysis is to automatically
determine subject's sentiment (e.g., positive, negative, or neutral) towards a
particular aspect such as topic, product, movie, news etc. Deep learning has
recently emerged as a powerful machine learning technique to tackle a growing
demand of accurate sentiment analysis. However, limited work has been conducted
to apply deep learning algorithms to languages other than English, such as
Persian. In this work, two deep learning models (deep autoencoders and deep
convolutional neural networks (CNNs)) are developed and applied to a novel
Persian movie reviews dataset. The proposed deep learning models are analyzed
and compared with the state-of-the-art shallow multilayer perceptron (MLP)
based machine learning model. Simulation results demonstrate the enhanced
performance of deep learning over state-of-the-art MLP.
