We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment classifiers. The proposed model is a fusion of a fully supervised structured conditional model and its partially supervised counterpart. This allows for highly efficient estimation and inference algorithms with rich feature definitions. We describe the two variants as well as their component models and verify experimentally that both variants give significantly improved results for sentence-level sentiment analysis compared to all baselines.
REF combine fully and partially supervised structured conditional models for a joint classification of the polarity of whole reviews and the review's sentences.
Semi-supervised latent variable models for sentence-level sentiment analysis
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science