The State of the Art in Sentiment Visualization

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dc.contributor.authorKucher, Kostiantynen_US
dc.contributor.authorParadis, Caritaen_US
dc.contributor.authorKerren, Andreasen_US
dc.contributor.editorChen, Min and Benes, Bedrichen_US
dc.date.accessioned2018-04-05T12:48:36Z
dc.date.available2018-04-05T12:48:36Z
dc.date.issued2018
dc.description.abstractVisualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data.Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer‐reviewed publications together with an interactive web‐based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization.en_US
dc.description.documenttypestar
dc.description.number1
dc.description.sectionheadersArticles
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13217
dc.identifier.issn1467-8659
dc.identifier.pages71-96
dc.identifier.urihttps://doi.org/10.1111/cgf.13217
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13217
dc.publisher© 2018 The Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectsentiment visualization
dc.subjecttext visualization
dc.subjectsentiment analysis
dc.subjectopinion mining
dc.subject• Information systems → Sentiment analysis; • Human‐centred computing → Visualization techniques; • Human‐centred computing → Visual analytics; • Human‐centred computing → Information visualization; • Human‐centred computing → Visualization systems and tools
dc.titleThe State of the Art in Sentiment Visualizationen_US
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