Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

Ranjan Satapathy, Shweta Rajesh Pardeshi and Erik Cambria
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Ranjan Satapathy: Graphene AI, 28 Genting Ln, Singapore 349585, Singapore
Shweta Rajesh Pardeshi: Granular AI, Mumbai 410206, India
Erik Cambria: School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore

Future Internet, 2022, vol. 14, issue 7, 1-10

Abstract: 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.

Keywords: multitask learning; polarity detection; subjectivity detection; deep learning; market intelligence (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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