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Methodology and Applications of Visual Stance Analysis : An Interactive Demo

Kucher, Kostiantyn; Kerren, Andreas; Paradis, Carita LU and Sahlgren, Magnus (2016) International Symposium on Digital Humanities p.56-57
Abstract
Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression... (More)
Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.

Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. (Less)
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author
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
Digital humanities, Stance, Visualization, interaction, NLP, Visual analytics, Annotation, Classifier training
pages
2 pages
conference name
International Symposium on Digital Humanities
language
English
LU publication?
yes
id
d8d6fe78-62b5-4299-b757-48614dcd5e6f
alternative location
https://lnu.se/contentassets/60702fb657fe49539530eaa834fda8ef/abstracts-final.pdf#page=56
date added to LUP
2016-11-28 20:38:42
date last changed
2017-11-14 09:49:15
@misc{d8d6fe78-62b5-4299-b757-48614dcd5e6f,
  abstract     = {Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.<br/><br/>Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. },
  author       = {Kucher, Kostiantyn and Kerren, Andreas and Paradis, Carita and Sahlgren, Magnus},
  keyword      = {Digital humanities, Stance,Visualization,interaction,NLP,Visual analytics,Annotation,Classifier training},
  language     = {eng},
  pages        = {56--57},
  title        = {Methodology and Applications of Visual Stance Analysis : An Interactive Demo},
  year         = {2016},
}