Visualization of Text Streams: A Survey

Šilić, Artur; Bašić, Bojana Dalbelo

doi:10.1007/978-3-642-15390-7_4

Cited by 32 publications

(20 citation statements)

References 49 publications

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“…Alencar et al [21] Gan et al [22] Nualart-Vilaplana et al [23] Cau and Cui [24] Federico et al [25] Šilić and Bašić [5] Wanner et al [26] Kucher and Kerren [4] Kucher et al [9] Liu et al [11] Jänicke et al [27] Jänicke et al [10] Sun et al [15] Liu et al [16]…”

Section: Document-centered User Task Analysis Multi-faceted Cross-dismentioning

confidence: 99%

“…However, the approaches listed in the Text Visualization Browser mainly come from the data visualization community and generally do not include literature from other communities-particularly from the digital humanities. The number of text literature surveys has grown since the first survey was published in 2010 by Šilić and Bašić [5] as shown in Figure 1. Collectively with duplicates, the surveys cite and review 1288 text visualization approaches.…”

Section: Introductionmentioning

confidence: 99%

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SoS TextVis: An Extended Survey of Surveys on Text Visualization

Alharbi

Laramee

2019

Computers

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Text visualization is a rapidly growing sub-field of information visualization and visual analytics. There are many approaches and techniques introduced every year to address a wide range of challenges and analysis tasks, enabling researchers from different disciplines to obtain leading-edge knowledge from digitized collections of text. This can be challenging particularly when the data is massive. Additionally, the sources of digital text have spread substantially in the last decades in various forms, such as web pages, blogs, twitter, email, electronic publications, and digitized books. In response to the explosion of text visualization research literature, the first text visualization survey article was published in 2010. Furthermore, there are a growing number of surveys that review existing techniques and classify them based on text research methodology. In this work, we aim to present the first Survey of Surveys (SoS) that review all of the surveys and state-of-the-art papers on text visualization techniques and provide an SoS classification. We study and compare the 14 surveys, and categorize them into five groups: (1) Document-centered, (2) user task analysis, (3) cross-disciplinary, (4) multi-faceted, and (5) satellite-themed. We provide survey recommendations for researchers in the field of text visualization. The result is a very unique, valuable starting point and overview of the current state-of-the-art in text visualization research literature.

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Section: Document-centered User Task Analysis Multi-faceted Cross-dismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SoS TextVis: An Extended Survey of Surveys on Text Visualization

Alharbi

Laramee

2019

Computers

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“…Several surveys mention sentiment and affect analysis as a potential feature extraction method for text visualization, for instance, the paper by Risch et al . [RKPW08] in the context of visual analytics (without any examples) or the paper by Šilić and Bašić [ŠB10] in connection with text stream visualization (with a single example). Given the total amount of work on sentiment visualization, there is clearly a gap in this area in the visualization survey literature.…”

Section: Related Workmentioning

confidence: 99%

The State of the Art in Sentiment Visualization

Kucher

Paradis

Kerren

2017

Computer Graphics Forum

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“…Šilić and Bašić's 2010 survey of text stream visualization defined three kinds of text being visualized -text collections, single texts, and short-interval text streams [15].…”

Section: B Streaming Text Analysismentioning

confidence: 99%

Massively scalable near duplicate detection in streams of documents using MDSH

Bogen

Symons

McKenzie

et al. 2013

2013 IEEE International Conference on Big Data

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In a world where large-scale text collections are not only becoming ubiquitous but also are growing at increasing rates, near duplicate documents are becoming a growing concern that has the potential to hinder many different information filtering tasks. While others have tried to address this problem, prior techniques have only been used on limited collection sizes and static cases. We will briefly describe the problem in the context of Open Source analysis along with our additional constraints for performance. In this work we propose two variations on Multi-dimensional Spectral Hash (MDSH) tailored for working on extremely large, growing sets of text documents. We analyze the memory and runtime characteristics of our techniques and provide an informal analysis of the quality of the near-duplicate clusters produced by our techniques.

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Visualization of Text Streams: A Survey

Cited by 32 publications

References 49 publications

SoS TextVis: An Extended Survey of Surveys on Text Visualization

SoS TextVis: An Extended Survey of Surveys on Text Visualization

The State of the Art in Sentiment Visualization

Massively scalable near duplicate detection in streams of documents using MDSH

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