  Due to the increased availability of online reviews, sentiment analysis had
been witnessed a booming interest from the researchers. Sentiment analysis is a
computational treatment of sentiment used to extract and understand the
opinions of authors. While many systems were built to predict the sentiment of
a document or a sentence, many others provide the necessary detail on various
aspects of the entity (i.e. aspect-based sentiment analysis). Most of the
available data resources were tailored to English and the other popular
European languages. Although Persian is a language with more than 110 million
speakers, to the best of our knowledge, there is a lack of public dataset on
aspect-based sentiment analysis for Persian. This paper provides a manually
annotated Persian dataset, Pars-ABSA, which is verified by 3 native Persian
speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827
neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this
paper reports the performance of some state-of-the-art aspect-based sentiment
analysis methods with a focus on deep learning, on Pars-ABSA. The obtained
results are impressive compared to similar English state-of-the-art.
