Utilizing distilBert transformer model for sentiment classification of COVID-۱۹’s Persian open-text responses
Publish place: The 7th International Conference on Science and Technology of Electrical, Computer and Mechanical Engineering of Iran
Publish Year: 1401
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
UTCONF07_069
تاریخ نمایه سازی: 20 اردیبهشت 1402
Abstract:
The COVID-۱۹ pandemic has caused drastic alternations in human’s life in all aspects. The government’s laws in this regard affected the lifestyle of all people. Due to this fact studying about the sentiment of individuals is important to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed a 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 comparison, and our best model could gain accuracy: ۰.۸۲۴, Precision: ۰.۸۲۴, Recall: ۰.۷۹۸ and F۱score: ۰.۸۰۴
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Authors
Fatemeh Sadat Masoumi
Department of Computer science, Allameh Tabatabe’I University
Mohammad Bahrani
Department of Computer science, Allameh Tabatabe’I University