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This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text.
Chronic lyme disease: misconceptions and challenges for patient management
J. J. Halperin · 2015
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
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A multiple streams approach to understanding the issues and challenges of lyme disease management in canada’s maritime provinces
M. Levesque and M. Klohn · 2019
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Post-treatment lyme disease as a model for persistent symptoms in lyme disease
A. W. Rebman and J. N. Aucott · 2020
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Transformers: State-of-the-art natural language processing
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. L. Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush · 2020
Cited alongside, same era.
Bidirectional encoder representations from transformers (bert) language model for sentiment analysis task
M. D. Deepa et al · 2021
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A review of post-treatment lyme disease syndrome and chronic lyme disease for the practicing immunologist
K. H. Wong, E. D. Shapiro, and G. K. Soffer · 2022
Later among the works it cites.
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