Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding

01/14/2022
by   Ranjan Satapathy, et al.
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Multitask learning often helps improve the performance of related tasks as these often have inter-dependence on each other and perform better when solved in a joint framework. In this paper, we present a deep multitask learning framework that jointly performs polarity and subjective detection. We propose an attention-based multitask model for predicting polarity and subjectivity. The input sentences are transformed into vectors using pre-trained BERT and Glove embeddings, and the results depict that BERT embedding based model works better than the Glove based model. We compare our approach with state-of-the-art models in both subjective and polarity classification single-task and multitask frameworks. The proposed approach reports baseline performances for both polarity detection and subjectivity detection.

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