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StanceVis Prime: Visual Analysis of Sentiment and Stance in Social Media Texts
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-VAESS)ORCID iD: 0000-0002-1907-7820
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-VAESS)ORCID iD: 0000-0002-2901-935X
Lund University, Sweden.ORCID iD: 0000-0002-7240-9003
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (ISOVIS;DISA-VAESS)ORCID iD: 0000-0002-0519-2537
2020 (English)In: Journal of Visualization, ISSN 1343-8875, E-ISSN 1875-8975, Vol. 23, no 6, p. 1015-1034Article in journal (Refereed) Published
Abstract [en]

Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest for this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by existing approaches. The challenges associated with this problem include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 23, no 6, p. 1015-1034
Keywords [en]
visual analytics, visualization, information visualization, interaction, sentiment analysis, stance analysis, text mining, natural language processing
National Category
Computer Sciences Human Computer Interaction Language Technology (Computational Linguistics)
Research subject
Computer Science, Information and software visualization
Identifiers
URN: urn:nbn:se:lnu:diva-96545DOI: 10.1007/s12650-020-00684-5ISI: 000562677200002Scopus ID: 2-s2.0-85089857634OAI: oai:DiVA.org:lnu-96545DiVA, id: diva2:1444197
Projects
StaViCTA
Funder
Swedish Research Council, 2012-5659Available from: 2020-06-20 Created: 2020-06-20 Last updated: 2021-05-06Bibliographically approved

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Kucher, KostiantynMartins, Rafael MessiasKerren, Andreas

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