Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
This work provides a strong specific gain in aspect-based sentiment analysis for researchers and applications dealing with the Persian language, where resources are scarce.
The paper addresses aspect-based sentiment analysis (ABSA) for the Persian language, a task with limited prior research. By leveraging a pre-trained Pars-BERT model and a natural language inference auxiliary sentence (NLI-M) input, the authors achieved an accuracy of 91% on the Pars-ABSA dataset, representing a 5.5% absolute improvement over previous state-of-the-art methods.
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.