Multitask Learning for Fine-Grained Twitter Sentiment Analysis
arXiv:1707.03569 · doi:10.1145/3077136.3080702
Abstract
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that such classification tasks are correlated and we propose a multitask approach based on a recurrent neural network that benefits 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 fine-grained sentiment classification problem.
International ACM SIGIR Conference on Research and Development in Information Retrieval 2017