  This paper focuses on how to extract opinions over each Persian
sentence-level text. Deep learning models provided a new way to boost the
quality of the output. However, these architectures need to feed on big
annotated data as well as an accurate design. To best of our knowledge, we do
not merely suffer from lack of well-annotated Persian sentiment corpus, but
also a novel model to classify the Persian opinions in terms of both multiple
and binary classification. So in this work, first we propose two novel deep
learning architectures comprises of bidirectional LSTM and CNN. They are a part
of a deep hierarchy designed precisely and also able to classify sentences in
both cases. Second, we suggested three data augmentation techniques for the
low-resources Persian sentiment corpus. Our comprehensive experiments on three
baselines and two different neural word embedding methods show that our data
augmentation methods and intended models successfully address the aims of the
research.
