Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses
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Abstract (arXiv)
The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 16 Dec 2022.
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References · 9
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- 2019Axial Attention in Multidimensional Transformerscited by 358
- 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 267
- 2020FastBERT: a Self-distilling BERT with Adaptive Inference Timecited by 226
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