1. Multi Task Mutual Learning for Joint Sentiment Classification and Topic Detection
- Author
-
Yulan He, Ruifeng Xu, Jiyun Zhou, Leng Jia, and Lin Gui
- Subjects
Topic model ,Perplexity ,Artificial neural network ,business.industry ,Computer science ,Probabilistic logic ,Multi-task learning ,Context (language use) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Semantics ,Computer Science Applications ,Computational Theory and Mathematics ,020204 information systems ,Classifier (linguistics) ,0202 electrical engineering, electronic engineering, information engineering ,Artificial intelligence ,business ,computer ,Information Systems - Abstract
Recently, advances in neural network approaches have achieved many successes in both sentiment classification and probabilistic topic modelling. 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 modelling. 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 the classifier 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 accuracy and topic modelling evaluation results including perplexity and topic coherence measures. The proposed framework also extracts more interpretable topics compared to other conventional topic models and neural topic models.
- Published
- 2022