# Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language

arXiv: https://arxiv.org/abs/2012.07510 · HTML: https://mlsanity.com/paper/2012.07510/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets](https://mlsanity.com/paper/2212.06041/) (2022)
- [KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis](https://mlsanity.com/paper/2509.16804/) (2025)
- [KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering](https://mlsanity.com/paper/2407.00342/) (2024)
- [PABSA: Hybrid Framework for Persian Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2510.04291/) (2025)
- [Aspect-Based Sentiment Analysis for Future Tourism Experiences: A BERT-MoE Framework for Persian User Reviews](https://mlsanity.com/paper/2602.12778/) (2026)

## Abstract (arXiv)

Aspect-based sentiment analysis (ABSA) is a more detailed task in sentiment analysis, by identifying opinion polarity toward a certain aspect in a text. This method is attracting more attention from the community, due to the fact that it provides more thorough and useful information. However, there are few language-specific researches on Persian language. The present research aims to improve the ABSA on the Persian Pars-ABSA dataset. This research shows the potential of using pre-trained BERT model and taking advantage of using sentence-pair input on an ABSA task. The results indicate that employing Pars-BERT pre-trained model along with natural language inference auxiliary sentence (NLI-M) could boost the ABSA task accuracy up to 91% which is 5.5% (absolute) higher than state-of-the-art studies on Pars-ABSA dataset.