Methodology and Applications of Visual Stance Analysis: An Interactive Demo

Research output: Contribution to conferencePoster


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.


External organisations
  • Linnaeus University
  • Gagavai AB
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Specific Languages
  • Engineering and Technology


  • Digital humanities, Stance, Visualization, interaction, NLP, Visual analytics, Annotation, Classifier training
Original languageEnglish
Number of pages2
Publication statusPublished - 2016
Publication categoryResearch
EventInternational Symposium on Digital Humanities - Växjö City Library , Växjö, Sweden
Duration: 2016 Nov 72016 Nov 8


ConferenceInternational Symposium on Digital Humanities
Internet address

Related projects

Carita Paradis, Andreas Kerren, Magnus Sahlgren, Kostiantyn Kucher, Maria Skeppstedt & Vasiliki Simaki

Swedish Research Council


Project: Research

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