StanceVis Prime: visual analysis of sentiment and stance in social media texts
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R E G UL A R P A P E R
Kostiantyn Kucher
•
Rafael M. Martins
•
Carita Paradis
•
Andreas Kerren
StanceVis Prime: visual analysis of sentiment and stance in social media texts
Received: 12 February 2020 / Revised: 25 May 2020 / Accepted: 16 June 2020 The Author(s) 2020
Abstract 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 in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the 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. Keywords Stance visualization Sentiment visualization Text visualization Stance analysis Sentiment analysis Opinion mining Visualization Interaction NLP Text analytics
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12650-020-00684-5) contains supplementary material, which is available to authorized users. K. Kucher (&) R. M. Martins A. Kerren Department of Computer Science and Media Technology, Linnaeus University, Va¨xjo¨, Sweden E-mail: [email protected] C. Paradis Centre for Languages and Literature, Lund University, Lund, Sweden
K. Kucher et al.
1 Introduction The recent years have demonstrated how massively available digital communication channels, such as so
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