Bridging Text Visualization and Mining: A Task-Driven Survey

Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and...

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Vydané v:IEEE transactions on visualization and computer graphics Ročník 25; číslo 7; s. 2482 - 2504
Hlavní autori: Liu, Shixia, Wang, Xiting, Collins, Christopher, Dou, Wenwen, Ouyang, Fangxin, El-Assady, Mennatallah, Jiang, Liu, Keim, Daniel A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Analytics
Data visualization
Interdisciplinary studies
Mathematical analysis
Phosphates
Regulatory reform
Site planning
Task analysis
Taxonomy
Text mining
Visual tasks
visual text analytics
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Abstract Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.
AbstractList Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.
Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.
Author El-Assady, Mennatallah
Jiang, Liu
Wang, Xiting
Liu, Shixia
Dou, Wenwen
Collins, Christopher
Ouyang, Fangxin
Keim, Daniel A.
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  orcidid: 0000-0003-4499-6420
  surname: Liu
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  organization: Tsinghua University, Beijing, P. R. China
– sequence: 2
  givenname: Xiting
  surname: Wang
  fullname: Wang, Xiting
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  organization: Microsoft Research, Beijing, China
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  orcidid: 0000-0002-4520-7000
  surname: Collins
  fullname: Collins, Christopher
  email: christopher.collins@uoit.ca
  organization: University of Ontario, Oshawa, ON, Canada
– sequence: 4
  givenname: Wenwen
  orcidid: 0000-0003-0319-9484
  surname: Dou
  fullname: Dou, Wenwen
  email: wdou1@uncc.edu
  organization: University of North Carolina at Charlotte, Charlotte, NC
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  givenname: Fangxin
  surname: Ouyang
  fullname: Ouyang, Fangxin
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  organization: Tsinghua University, Beijing, P. R. China
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  givenname: Mennatallah
  surname: El-Assady
  fullname: El-Assady, Mennatallah
  email: menna.el-assady@uni-konstanz.de
  organization: UOIT, Oshawa, ON, Canada
– sequence: 7
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  orcidid: 0000-0002-7189-7762
  surname: Jiang
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  organization: Tsinghua University, Beijing, P. R. China
– sequence: 8
  givenname: Daniel A.
  surname: Keim
  fullname: Keim, Daniel A.
  email: keim@uni-konstanz.de
  organization: University of Konstanz, Konstanz, Germany
BackLink https://www.ncbi.nlm.nih.gov/pubmed/29993887$$D View this record in MEDLINE/PubMed
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Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019
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Snippet Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of...
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SubjectTerms Analytics
Data visualization
Interdisciplinary studies
Mathematical analysis
Phosphates
Regulatory reform
Site planning
Task analysis
Taxonomy
Text mining
Visual tasks
visual text analytics
Visualization
Title Bridging Text Visualization and Mining: A Task-Driven Survey
URI https://ieeexplore.ieee.org/document/8356097
https://www.ncbi.nlm.nih.gov/pubmed/29993887
https://www.proquest.com/docview/2231868376
https://www.proquest.com/docview/2068341768
Volume 25
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