Visual Analysis of Sentiment and Stance in Social Media Texts

Research output: Contribution to conferencePoster


Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.


External organisations
  • Linnaeus University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Electrical Engineering, Electronic Engineering, Information Engineering
  • Languages and Literature


  • visualization, visual analytics, text mining, sentiment, stance, Natural language processing
Original languageEnglish
Publication statusPublished - 2018
Publication categoryResearch
EventEuroVis 2018: 20th EG/VGTC Conference on Visualization - Masaryk University, Brno, Czech Republic
Duration: 2018 Jun 42018 Jun 8
Conference number: 20


ConferenceEuroVis 2018
CountryCzech Republic
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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Related prizes

Vincenzo Pietrogiovanni (Recipient), 2014

Prizes and Distinctions: National/international honour

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