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

• URL: http://arxiv.org/abs/2012.07510v1
• Date: Wed, 2 Dec 2020 16:47:20 GMT
• Title: Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language
• Authors: H. Jafarian, A. H. Taghavi, A. Javaheri and R. Rawassizadeh
• Abstract summary: 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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