The State of the Art in Sentiment Visualization
Abstract
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. 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.
BibTeX
@article {10.1111:cgf.13217,
journal = {Computer Graphics Forum},
title = {{The State of the Art in Sentiment Visualization}},
author = {Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas},
year = {2018},
publisher = {© 2018 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13217}
}
journal = {Computer Graphics Forum},
title = {{The State of the Art in Sentiment Visualization}},
author = {Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas},
year = {2018},
publisher = {© 2018 The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.13217}
}
Collections
Related items
Showing items related by title, author, creator and subject.
-
Visualizing for the Non-Visual: Enabling the Visually Impaired to Use Visualization
Choi, Jinho; Jung, Sanghun; Park, Deok Gun; Choo, Jaegul; Elmqvist, Niklas (The Eurographics Association and John Wiley & Sons Ltd., 2019)The majority of visualizations on the web are still stored as raster images, making them inaccessible to visually impaired users. We propose a deep-neural-network-based approach that automatically recognizes key elements ... -
Query by Visual Words: Visual Search for Scatter Plot Visualizations
Shao, Lin; Schleicher, Timo; Schreck, Tobias (The Eurographics Association, 2016)Finding interesting views in large collections of data visualizations, e.g., scatter plots, is challenging. Recently, ranking views based on heuristic quality measures has been proposed. However, quality measures may fail ... -
Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics
Badam, Sriram Karthik; Elmqvist, Niklas; Fekete, Jean-Daniel (The Eurographics Association and John Wiley & Sons Ltd., 2017)Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then ...