The state of the art in sentiment visualization
Kucher, Kostiantyn ; Paradis, Carita LU
and Kerren, Andreas
(2018)
In Computer Graphics Forum
37(1).
p.71-96
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/cd47f50c-11da-4e3d-8b7e-3bbd9d180d5a
- author
- Kucher, Kostiantyn
; Paradis, Carita
LU
and Kerren, Andreas
- organization
- publishing date
- 2018
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- sentiment visualization, text visualization, sentiment analysis, opinion mining
- in
- Computer Graphics Forum
- volume
- 37
- issue
- 1
- pages
- 71 - 96
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85051505723
- ISSN
- 1467-8659
- DOI
- 10.1111/cgf.13217
- project
- StaViCTA - Advances in the description and explanation of stance in discourse using visual and computational text analytics
- language
- English
- LU publication?
- yes
- id
- cd47f50c-11da-4e3d-8b7e-3bbd9d180d5a
- date added to LUP
- 2017-05-15 21:03:03
- date last changed
- 2022-04-09 08:10:39
@article{cd47f50c-11da-4e3d-8b7e-3bbd9d180d5a,
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<br/>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.}},
author = {{Kucher, Kostiantyn and Paradis, Carita and Kerren, Andreas}},
issn = {{1467-8659}},
keywords = {{sentiment visualization; text visualization; sentiment analysis; opinion mining}},
language = {{eng}},
number = {{1}},
pages = {{71--96}},
publisher = {{Wiley-Blackwell}},
series = {{Computer Graphics Forum}},
title = {{The state of the art in sentiment visualization}},
url = {{http://dx.doi.org/10.1111/cgf.13217}},
doi = {{10.1111/cgf.13217}},
volume = {{37}},
year = {{2018}},
}