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

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  • 11 Instituto de Matemáticas de la Universidad de Sevilla (IMUS) , Seville, Spain .

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Summary

This study introduces a new online framework for visualizing news data over time. It uses Natural Language Processing and mathematical optimization to represent word importance and semantic relationships dynamically.

Keywords:
news data streamsonline visualizationrelatednessvisual stabilityword importance

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Area of Science:

  • Computational linguistics
  • Data visualization
  • Mathematical optimization

Background:

  • Effective visualization of large news datasets over time is challenging.
  • Existing methods may not adequately capture dynamic word importance and semantic relatedness.

Purpose of the Study:

  • To develop a novel online framework for visualizing news data over a time horizon.
  • To represent word importance and semantic relatedness dynamically using mathematical optimization.

Main Methods:

  • Natural Language Processing (NLP) for word extraction and attribute calculation (importance, relatedness).
  • Mathematical Optimization model representing words as circles with time-varying areas.
  • Numerical optimization approach guiding word placement based on semantic relatedness, spread, and visual stability.

Main Results:

  • A flexible, incremental, and scalable visualization framework.
  • Demonstration of dynamic word importance and semantic relatedness visualization.
  • Successful application to Danish news data across three case studies.

Conclusions:

  • The developed framework offers an effective method for visualizing dynamic news data.
  • The approach enhances understanding of temporal trends in word importance and relationships.
  • The system is interactive and adaptable for user engagement.