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Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning

The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized l...

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Detalles Bibliográficos
Autores principales: Prottasha, Nusrat Jahan, Sami, Abdullah As, Kowsher, Md, Murad, Saydul Akbar, Bairagi, Anupam Kumar, Masud, Mehedi, Baz, Mohammed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185586/
https://www.ncbi.nlm.nih.gov/pubmed/35684778
http://dx.doi.org/10.3390/s22114157
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author Prottasha, Nusrat Jahan
Sami, Abdullah As
Kowsher, Md
Murad, Saydul Akbar
Bairagi, Anupam Kumar
Masud, Mehedi
Baz, Mohammed
author_facet Prottasha, Nusrat Jahan
Sami, Abdullah As
Kowsher, Md
Murad, Saydul Akbar
Bairagi, Anupam Kumar
Masud, Mehedi
Baz, Mohammed
author_sort Prottasha, Nusrat Jahan
collection PubMed
description The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT’s transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms.
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spelling pubmed-91855862022-06-11 Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning Prottasha, Nusrat Jahan Sami, Abdullah As Kowsher, Md Murad, Saydul Akbar Bairagi, Anupam Kumar Masud, Mehedi Baz, Mohammed Sensors (Basel) Article The growth of the Internet has expanded the amount of data expressed by users across multiple platforms. The availability of these different worldviews and individuals’ emotions empowers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized labeled data in the Bangla NLP domain. The majority of the existing Bangla research has relied on models of deep learning that significantly focus on context-independent word embeddings, such as Word2Vec, GloVe, and fastText, in which each word has a fixed representation irrespective of its context. Meanwhile, context-based pre-trained language models such as BERT have recently revolutionized the state of natural language processing. In this work, we utilized BERT’s transfer learning ability to a deep integrated model CNN-BiLSTM for enhanced performance of decision-making in sentiment analysis. In addition, we also introduced the ability of transfer learning to classical machine learning algorithms for the performance comparison of CNN-BiLSTM. Additionally, we explore various word embedding techniques, such as Word2Vec, GloVe, and fastText, and compare their performance to the BERT transfer learning strategy. As a result, we have shown a state-of-the-art binary classification performance for Bangla sentiment analysis that significantly outperforms all embedding and algorithms. MDPI 2022-05-30 /pmc/articles/PMC9185586/ /pubmed/35684778 http://dx.doi.org/10.3390/s22114157 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Prottasha, Nusrat Jahan
Sami, Abdullah As
Kowsher, Md
Murad, Saydul Akbar
Bairagi, Anupam Kumar
Masud, Mehedi
Baz, Mohammed
Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title_full Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title_fullStr Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title_full_unstemmed Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title_short Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
title_sort transfer learning for sentiment analysis using bert based supervised fine-tuning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185586/
https://www.ncbi.nlm.nih.gov/pubmed/35684778
http://dx.doi.org/10.3390/s22114157
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