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.

本研究提供了一个经过三名母语为波斯语的人员验证的波斯语数据集（即 Pars-ABSA），并基于深度学习的一些最先进的基于方面的情感分析方法进行评估，结果显示与类似的英语最新成果相比表现出优异的表现。