Methodology and Applications of Visual Stance Analysis: An Interactive Demo
2016 (English)In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, 56-57 p.Conference paper, Poster (Refereed)
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.
Place, publisher, year, edition, pages
Linnaeus University , 2016. 56-57 p.
Digital humanities, Stance, Visualization, Interaction, NLP, Visual analytics, Annotation, Classifier training
Computer Science Human Computer Interaction Language Technology (Computational Linguistics)
Research subject Computer Science, Information and software visualization
IdentifiersURN: urn:nbn:se:lnu:diva-57763OAI: oai:DiVA.org:lnu-57763DiVA: diva2:1044059
International Symposium on Digital Humanities, Växjö, Sweden, November 7-8, 2016
FunderSwedish Research Council, 2012-5659