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...

Celý popis

Uložené v:
Podrobná bibliografia
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2018.2834341