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.
@article{arxiv.2201.05363,
title = {Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding},
author = {Ranjan Satapathy and Shweta Pardeshi and Erik Cambria},
journal= {arXiv preprint arXiv:2201.05363},
year = {2022}
}