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 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.

Automated summary

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.