  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.
