@inproceedings{shangipour-ataei-etal-2022-pars,
title = "Pars-{ABSA}: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on {F}arsi Product Reviews",
author = "Shangipour ataei, Taha and
Darvishi, Kamyar and
Javdan, Soroush and
Minaei-Bidgoli, Behrouz and
Eetemadi, Sauleh",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.763",
pages = "7056--7060",
abstract = "Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from 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 Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, annotated and verified by three native Farsi 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 aspect-based sentiment analysis methods focusing on transfer learning on Pars-ABSA.",
}
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%0 Conference Proceedings
%T Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews
%A Shangipour ataei, Taha
%A Darvishi, Kamyar
%A Javdan, Soroush
%A Minaei-Bidgoli, Behrouz
%A Eetemadi, Sauleh
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F shangipour-ataei-etal-2022-pars
%X Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from 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 Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, annotated and verified by three native Farsi 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 aspect-based sentiment analysis methods focusing on transfer learning on Pars-ABSA.
%U https://aclanthology.org/2022.lrec-1.763
%P 7056-7060
Markdown (Informal)
[Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews](https://aclanthology.org/2022.lrec-1.763) (Shangipour ataei et al., LREC 2022)
ACL