Täckström, Oscar and McDonald, Ryan (2011) Discovering fine-grained sentiment with latent variable structured prediction models. [SICS Report]
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.
|Item Type:||SICS Report|
|Uncontrolled Keywords:||Sentiment analysis, Latent variables, Structured conditional models|
|Deposited By:||Vicki Carleson|
|Deposited On:||12 Jan 2011 13:31|
|Last Modified:||12 Jan 2011 13:49|
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