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Detection of Stance and Sentiment Modifiers in Political Blogs
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0001-6164-7762
Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University. (ISOVIS)ORCID iD: 0000-0002-8998-3618
Lund University.ORCID iD: 0000-0002-7240-9003
Linnaeus University, Faculty of Technology, Department of Computer Science. (ISOVIS)ORCID iD: 0000-0002-0519-2537
2017 (English)Conference paper (Refereed)
Abstract [en]

The automatic detection of seven types of modifiers was studied: Certainty, Uncertainty, Hypotheticality, Prediction, Recommendation, Concession/Contrast and Source. A classifier aimed at detecting local cue words that signal the categories was the most successful method for five of the categories. For Prediction and Hypotheticality, however, better results were obtained with a classifier trained on tokens and bi-grams present in the entire sentence. Unsupervised cluster features were shown useful for the categories Source and Uncertainty, when a subset of the training data available was used. However, when all of the 2,095 sentences that had been actively selected and manually annotated were used as training data, the cluster features had a very limited effect. Some of the classification errors made by the models would be possible to avoid by extending the training data set, while other features and feature representations, as well as the incorporation of pragmatic knowledge, would be required for other error types. 

Place, publisher, year, edition, pages
Springer, 2017.
Keyword [en]
stance modifiers, sentiment modifiers, active learning, unsupervised features, resource-aware natural language processing
National Category
Language Technology (Computational Linguistics) Computer Science
Research subject
Computer Science, Information and software visualization; Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-64582OAI: oai:DiVA.org:lnu-64582DiVA: diva2:1104283
Conference
19th International Conference on Speech and Computer (SPECOM '17), 12-16 September 2017, Hatfield, Hertfordshire, UK
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659
Note

TO BE PUBLISHED!

Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2017-06-01

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Skeppstedt, MariaSimaki, VasilikiParadis, CaritaKerren, Andreas
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf