On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization

Emilio Carrizosa, Vanesa Guerrero, Daniel Hardt, Dolores Romero Morales

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.
In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.
LanguageEnglish
JournalBig Data
Volume6
Issue number2
Pages139-158
Number of pages20
ISSN2167-6461
DOIs
StatePublished - 2018

Bibliographical note

CBS Library does not have access to the material

Keywords

  • News data streams
  • Online visualization
  • Relatedness
  • Visual stability
  • Word importance

Cite this

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title = "On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization",
abstract = "In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.",
keywords = "News data streams, Online visualization, Relatedness, Visual stability, Word importance, News data streams, Online visualization, Relatedness, Visual stability, Word importance",
author = "Emilio Carrizosa and Vanesa Guerrero and Daniel Hardt and Morales, {Dolores Romero}",
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year = "2018",
doi = "10.1089/big.2018.0017",
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volume = "6",
pages = "139--158",
journal = "Big Data",
issn = "2167-6461",
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On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization. / Carrizosa, Emilio; Guerrero, Vanesa; Hardt, Daniel; Morales, Dolores Romero.

In: Big Data, Vol. 6, No. 2, 2018, p. 139-158.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization

AU - Carrizosa,Emilio

AU - Guerrero,Vanesa

AU - Hardt,Daniel

AU - Morales,Dolores Romero

N1 - CBS Library does not have access to the material

PY - 2018

Y1 - 2018

N2 - In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.

AB - In this article we develop a novel online framework to visualize news data over a time horizon. First, we perform a Natural Language Processing analysis, wherein the words are extracted, and their attributes, namely the importance and the relatedness, are calculated. Second, we present a Mathematical Optimization model for the visualization problem and a numerical optimization approach. The model represents the words using circles, the time-varying area of which displays the importance of the words in each time period. Word location in the visualization region is guided by three criteria, namely, the accurate representation of semantic relatedness, the spread of the words in the visualization region to improve the quality of the visualization, and the visual stability over the time horizon. Our approach is flexible, allowing the user to interact with the display, as well as incremental and scalable. We show results for three case studies using data from Danish news sources.

KW - News data streams

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KW - News data streams

KW - Online visualization

KW - Relatedness

KW - Visual stability

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