Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
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Fecha
2024-06
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Springer Science and Business Media Deutschland GmbH
Resumen
COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803
Palabras clave
Deep learning
DistilBERT
Natural language processing
Sentiment analysis
Transformer
DistilBERT
Natural language processing
Sentiment analysis
Transformer
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Materias
Cita
Jojoa, M., Eftekhar, P., Nowrouzi-Kia, B., & Garcia-Zapirain, B. (2024). Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization. AI and Society, 39(3), 883-890. https://doi.org/10.1007/S00146-022-01594-W