Traditional sentiment analysis approaches tackle problems like ternary (3-category) and ne-grained (5-category) classi cation by learning the tasks separately. We argue that such classi cation tasks are correlated and we propose a multitask approach based on a recurrent neural network that bene ts by jointly learning them. Our study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the ne-grained sentiment classi cation problem.
REF proposed a multi-task learning model for fine-grained sentiment analysis.
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
SIGIR '17
Computer Science