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A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification

Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in...

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Detalles Bibliográficos
Autores principales: Qasim, Rukhma, Bangyal, Waqas Haider, Alqarni, Mohammed A., Ali Almazroi, Abdulwahab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742153/
https://www.ncbi.nlm.nih.gov/pubmed/35013691
http://dx.doi.org/10.1155/2022/3498123
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author Qasim, Rukhma
Bangyal, Waqas Haider
Alqarni, Mohammed A.
Ali Almazroi, Abdulwahab
author_facet Qasim, Rukhma
Bangyal, Waqas Haider
Alqarni, Mohammed A.
Ali Almazroi, Abdulwahab
author_sort Qasim, Rukhma
collection PubMed
description Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.
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spelling pubmed-87421532022-01-09 A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification Qasim, Rukhma Bangyal, Waqas Haider Alqarni, Mohammed A. Ali Almazroi, Abdulwahab J Healthc Eng Research Article Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed. Hindawi 2022-01-07 /pmc/articles/PMC8742153/ /pubmed/35013691 http://dx.doi.org/10.1155/2022/3498123 Text en Copyright © 2022 Rukhma Qasim et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Qasim, Rukhma
Bangyal, Waqas Haider
Alqarni, Mohammed A.
Ali Almazroi, Abdulwahab
A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title_full A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title_fullStr A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title_full_unstemmed A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title_short A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
title_sort fine-tuned bert-based transfer learning approach for text classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8742153/
https://www.ncbi.nlm.nih.gov/pubmed/35013691
http://dx.doi.org/10.1155/2022/3498123
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