Multitask Learning for Fine-Grained Twitter Sentiment Analysis
This work addresses sentiment analysis for social media applications, but it is incremental as it builds on existing multitask and neural network methods.
The paper tackled the problem of fine-grained sentiment analysis on Twitter by proposing a multitask learning approach using a recurrent neural network, which improved state-of-the-art results for 5-category classification.
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.