Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model
Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5232 |
| _version_ | 1797596730263339008 |
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| author | Ali Areshey Hassan Mathkour |
| author_facet | Ali Areshey Hassan Mathkour |
| author_sort | Ali Areshey |
| collection | DOAJ |
| description | Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance. |
| first_indexed | 2024-03-11T02:57:09Z |
| format | Article |
| id | doaj.art-e34e4f55b405428c88f0a046bd04d041 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:09Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e34e4f55b405428c88f0a046bd04d0412023-11-18T08:34:21ZengMDPI AGSensors1424-82202023-05-012311523210.3390/s23115232Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) ModelAli Areshey0Hassan Mathkour1Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaSentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.https://www.mdpi.com/1424-8220/23/11/5232BERT modelsentiment analysismachine learningtransformerstransfer learning |
| spellingShingle | Ali Areshey Hassan Mathkour Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model Sensors BERT model sentiment analysis machine learning transformers transfer learning |
| title | Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model |
| title_full | Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model |
| title_fullStr | Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model |
| title_full_unstemmed | Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model |
| title_short | Transfer Learning for Sentiment Classification Using Bidirectional Encoder Representations from Transformers (BERT) Model |
| title_sort | transfer learning for sentiment classification using bidirectional encoder representations from transformers bert model |
| topic | BERT model sentiment analysis machine learning transformers transfer learning |
| url | https://www.mdpi.com/1424-8220/23/11/5232 |
| work_keys_str_mv | AT aliareshey transferlearningforsentimentclassificationusingbidirectionalencoderrepresentationsfromtransformersbertmodel AT hassanmathkour transferlearningforsentimentclassificationusingbidirectionalencoderrepresentationsfromtransformersbertmodel |