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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/d8d6fe78-62b5-4299-b757-48614dcd5e6f
- author
- Kucher, Kostiantyn
; Kerren, Andreas
; Paradis, Carita
LU
and Sahlgren, Magnus
- organization
- publishing date
- 2016
- 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
- conference location
- Växjö, Sweden
- conference dates
- 2016-11-07 - 2016-11-08
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- 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
- 2019-03-08 02:28:42
@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}},
keywords = {{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}},
url = {{https://lnu.se/contentassets/60702fb657fe49539530eaa834fda8ef/abstracts-final.pdf#page=56}},
year = {{2016}},
}