Exploring the Performance of Traditional Word Embeddings and Contextual Embedding Models for COVID-19 Sentiment Analysis-Reference-Cited by-同舟云学术

Exploring the Performance of Traditional Word Embeddings and Contextual Embedding Models for COVID-19 Sentiment Analysis

Published:2023 Issue: Volume: Page:469-478
ISSN:2367-3370
Container-title:International Conference on Advanced Intelligent Systems for Sustainable Development
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Author:

Bensalah Nouhaila,Ayad Habib,Adib Abdellah,Farouk Abdelhamid Ibn El

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-35248-5_42

Reference19 articles.

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3. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of Deep Bidirectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, pp. 4171–4186 (2019)

4. Gascun, C.F.D., Carr, M.J., Hall, W.W.: Chapter 161 - Influenza viruses. In: Infectious Diseases (Third Edition), pp. 1590–1597 (2010)

5. Ghani, N.A., Hamid, S., Hashem, I.A.T., Ahmed, E.: Social media big data analytics: a survey. Comput. Hum. Behav. 101, 417–428 (2019)

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