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 em-powers sentiment analysis. However, sentiment analysis becomes even more challenging due to a scarcity of standardized...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34888/ http://umpir.ump.edu.my/id/eprint/34888/1/Transfer%20learning%20for%20sentiment%20analysis%20using%20bert%20based%20supervised%20fine-tuning.pdf |
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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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 em-powers 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. |
| first_indexed | 2025-11-15T03:16:04Z |
| format | Article |
| id | ump-34888 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:16:04Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-348882022-11-08T08:42:48Z http://umpir.ump.edu.my/id/eprint/34888/ 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 QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) 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 em-powers 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 2022-06-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/34888/1/Transfer%20learning%20for%20sentiment%20analysis%20using%20bert%20based%20supervised%20fine-tuning.pdf Prottasha, Nusrat Jahan and Sami, Abdullah As and Kowsher, Md and Murad, Saydul Akbar and Bairagi, Anupam Kumar and Masud, Mehedi and Baz, Mohammed (2022) Transfer learning for sentiment analysis using bert based supervised fine-tuning. Sensors, 22 (11). pp. 1-19. ISSN 1424-8220. (Published) https://doi.org/10.3390/s22114157 https://doi.org/10.3390/s22114157 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) 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 | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/34888/ http://umpir.ump.edu.my/id/eprint/34888/ http://umpir.ump.edu.my/id/eprint/34888/ http://umpir.ump.edu.my/id/eprint/34888/1/Transfer%20learning%20for%20sentiment%20analysis%20using%20bert%20based%20supervised%20fine-tuning.pdf |