Polarity and subjectivity detection with multitask learning and BERT embedding
In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propo...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2023
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| 在線閱讀: | https://hdl.handle.net/10356/168631 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In recent years, deep learning-based sentiment analysis has received attention mainly because of the rise of social media and e-commerce. In this paper, we showcase the fact that the polarity detection and subjectivity detection subtasks of sentiment analysis are inter-related. To this end, we propose a knowledge-sharing-based multitask learning framework. To ensure high-quality knowledge sharing between the tasks, we use the Neural Tensor Network, which consists of a bilinear tensor layer that links the two entity vectors. We show that BERT-based embedding with our MTL framework outperforms the baselines and achieves a new state-of-the-art status in multitask learning. Our framework shows that the information across datasets for related tasks can be helpful for understanding task-specific features. |
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