# Multitask Learning for Fine-Grained Twitter Sentiment Analysis

arXiv: https://arxiv.org/abs/1707.03569 · HTML: https://mlsanity.com/paper/1707.03569/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2017.

## Built on by (most cited first, 5 of 5)

- [Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification](https://mlsanity.com/paper/2109.05427/) (2021)
- [Polarity and Subjectivity Detection with Multitask Learning and BERT Embedding](https://mlsanity.com/paper/2201.05363/) (2022)
- [Chinese Word Sense Embedding with SememeWSD and Synonym Set](https://mlsanity.com/paper/2206.14388/) (2022)
- [LaDiff ULMFiT: A Layer Differentiated training approach for ULMFiT](https://mlsanity.com/paper/2101.04965/) (2021)
- [MetaXT: Meta Cross-Task Transfer between Disparate Label Spaces](https://mlsanity.com/paper/2109.04240/) (2021)

## Abstract (arXiv)

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.