Multitask Learning for Fine-Grained Twitter Sentiment Analysis

By Georgios Balikas, Simon Moura, Massih-Reza Amini. 2017

94 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Olga Vechtomova
2. Hareesh Bahuleyan
3. Lili Mou
4. Vineet John

Citations in the corpus

• Disentangled Representation Learning for Non-Parallel Text Style Transfer (2018, methodology): “, 2017) and sentiment analysis (Balikas et al., 2017), among others.”