Multitask Learning for Fine-Grained Twitter Sentiment Analysis

This study demonstrates the potential of multitask models on this type of problems and improves the state-of-the-art results in the fine-grained sentiment classification problem.

Abstract

Traditional sentiment analysis approaches tackle problems like ternary\n(3-category) and fine-grained (5-category) classification by learning the tasks\nseparately. We argue that such classification tasks are correlated and we\npropose a multitask approach based on a recurrent neural network that benefits\nby jointly learning them. Our study demonstrates the potential of multitask\nmodels on this type of problems and improves the state-of-the-art results in\nthe fine-grained sentiment classification problem.\n