Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Information Retrieval
2017-07-13 v1 Computation and Language
Machine Learning
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.
Cite
@article{arxiv.1707.03569,
title = {Multitask Learning for Fine-Grained Twitter Sentiment Analysis},
author = {Georgios Balikas and Simon Moura and Massih-Reza Amini},
journal= {arXiv preprint arXiv:1707.03569},
year = {2017}
}
Comments
International ACM SIGIR Conference on Research and Development in Information Retrieval 2017