期刊
FUTURE INTERNET
卷 14, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/fi14070191
关键词
multitask learning; polarity detection; subjectivity detection; deep learning; market intelligence
In this paper, a knowledge-sharing-based multitask learning framework is proposed for polarity detection and subjectivity detection in sentiment analysis. The Neural Tensor Network is used to ensure high-quality knowledge sharing between the tasks, and the BERT-based embedding with the MTL framework outperforms the baselines in multitask learning. The framework demonstrates that information across datasets for related tasks can be helpful for understanding task-specific features.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据