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Discovering fine-grained sentiment with latent variable structured prediction models
RISE, Swedish ICT, SICS.
Number of Authors: 22011 (English)Conference paper, Published 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.
Keywords [en]
Sentiment analysis, Latent variables, Structured conditional models
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-23782OAI: oai:DiVA.org:ri-23782DiVA, id: diva2:1042859
Conference
The 33rd European Conference on Information Retrieval
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2020-12-01Bibliographically approved

Open Access in DiVA

fulltext(416 kB)110 downloads
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File name FULLTEXT01.pdfFile size 416 kBChecksum SHA-512
5e32edb3b8d7a5a59e691e0c03c016d211bffade8ead1cbd4e581db1ea2f1024659780c0860e7f6654b3f6e71cbfeab67e2b901676ff5911162c51c09a826995
Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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