Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection

Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modeling. On the one hand, latent topics derived from the global context of documents could be helpful in capturing more accurate word semantics and hence could potentially improve the sentiment classification accuracy. On the other hand, the word-level attention vectors obtained during the learning of sentiment classifiers could carry word-level polarity information and can be used to guide the discovery of topics in topic modeling. This paper proposes a multi-task learning framework which jointly learns a sentiment classifier and a topic model by making the word-level latent topic distributions in the topic model to be similar to the word-level attention vectors in sentiment classifiers through mutual learning. Experimental results on the Yelp and IMDB datasets verify the superior performance of the proposed framework over strong baselines on both sentiment classification and topic modeling. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.

Automated summary

This paper proposes a multi-task learning framework that jointly learns a sentiment classifier and a topic model, aiming to make the word-level latent topic distributions in the topic model similar to the word-level attention vectors in sentiment classifiers. The experimental results on Yelp and IMDB datasets demonstrate the superior performance of the proposed framework in both sentiment classification and topic modeling tasks.