Abstract
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) [10]. Experiments show that this technique reduces sentence classification errors by 22% relative to using a lexicon and 13% relative to machine-learning baselines.
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Täckström, O., McDonald, R. (2011). Discovering Fine-Grained Sentiment with Latent Variable Structured Prediction Models. In: Clough, P., et al. Advances in Information Retrieval. ECIR 2011. Lecture Notes in Computer Science, vol 6611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20161-5_37
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DOI: https://doi.org/10.1007/978-3-642-20161-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20160-8
Online ISBN: 978-3-642-20161-5
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