Discovering fine-grained sentiment with latent variable structured prediction models
Number of Authors: 2
2011 (English)Report (Other academic)
In this paper we investigate the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. Specifically, we show how sentence-level sentiment labels can be effectively learned from document-level supervision using hidden conditional random fields (HCRFs). Experiments show that this technique reduces sentence classification errors by 22\% relative to using a lexicon and by 13\% relative to machine-learning baselines. We provide a comprehensible description of the proposed probabilistic model and the features employed. Further, we describe the construction of a manually annotated test set, which was used in a thorough empirical investigation of the performance of the proposed model.
Place, publisher, year, edition, pages
Kista, Sweden: Swedish Institute of Computer Science , 2011, 14.
SICS Technical Report, ISSN 1100-3154 ; 2011:02
Sentiment analysis, Latent variables, Structured conditional models
Computer and Information Science
IdentifiersURN: urn:nbn:se:ri:diva-16094OAI: oai:DiVA.org:ri-16094DiVA: diva2:1038118