Visual Analysis of Text Annotations for Stance Classification with ALVA

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.


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

Subject classification (UKÄ) – MANDATORY

  • General Language Studies and Linguistics
  • Human Computer Interaction
  • Language Technology (Computational Linguistics)
Original languageEnglish
Title of host publicationEuroVis Posters 2016
EditorsTobias Isenberg, Filip Sadlo
PublisherEurographics - European Association for Computer Graphics
Number of pages3
ISBN (Electronic)978-3-03868-015-4
Publication statusPublished - 2016 Apr 28
Publication categoryResearch
EventEuroVis 2016, The 18th EG/VGTC Conference on Visualization - The Oosterpoort complex, Groningen, Netherlands
Duration: 2016 Jun 62016 Jun 10


ConferenceEuroVis 2016, The 18th EG/VGTC Conference on Visualization

Related projects

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

Swedish Research Council


Project: Research

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