Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian
This provides a resource for researchers working on Persian language processing, addressing a gap for a language with over 110 million speakers, but it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the lack of a public dataset for aspect-based sentiment analysis in Persian by introducing Pars-ABSA, a manually annotated dataset with 5,114 positive, 3,061 negative, and 1,827 neutral samples from 5,602 reviews, and reports baseline results from state-of-the-art methods that are impressive compared to English benchmarks.
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.