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Discovering fine-grained sentiment with latent variable structured prediction models
RISE, Swedish ICT, SICS.
Number of Authors: 2
2011 (English)Conference paper (Refereed)
Abstract [en]

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 13% relative to machine-learning baselines.

Place, publisher, year, edition, pages
2011, 9.
Keyword [en]
Sentiment analysis, Latent variables, Structured conditional models
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:ri:diva-23782OAI: oai:DiVA.org:ri-23782DiVA: diva2:1042859
Conference
The 33rd European Conference on Information Retrieval
Available from: 2016-10-31 Created: 2016-10-31

Open Access in DiVA

fulltext(416 kB)2 downloads
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File name FULLTEXT01.pdfFile size 416 kBChecksum SHA-512
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Type fulltextMimetype application/pdf

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McDonald, Ryan
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SICS
Computer and Information Science

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ReferencesLink to record
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