  Identification of user's opinions from natural language text has become an
exciting field of research due to its growing applications in the real world.
The research field is known as sentiment analysis and classification, where
aspect category detection (ACD) and aspect category polarity (ACP) are two
important sub-tasks of aspect-based sentiment analysis. The goal in ACD is to
specify which aspect of the entity comes up in opinion while ACP aims to
specify the polarity of each aspect category from the ACD task. The previous
works mostly propose separate solutions for these two sub-tasks. This paper
focuses on the ACD and ACP sub-tasks to solve both problems simultaneously. The
proposed method carries out multi-label classification where four different
deep models were employed and comparatively evaluated to examine their
performance. A dataset of Persian reviews was collected from CinemaTicket
website including 2200 samples from 14 categories. The developed models were
evaluated using the collected dataset in terms of example-based and label-based
metrics. The results indicate the high applicability and preference of the CNN
and GRU models in comparison to LSTM and Bi-LSTM.
