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.

中文翻译：

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通过数学优化构建新闻数据流的在线可视化地图。

在本文中，我们开发了一个新颖的在线框架来可视化一段时间内的新闻数据。首先，我们执行自然语言处理分析，其中提取单词，并计算它们的属性，即重要性和相关性。其次，我们提出了可视化问题的数学优化模型和数值优化方法。该模型使用圆圈表示单词，圆圈的时变区域显示每个时间段内单词的重要性。可视化区域中的单词定位受三个标准指导，即语义相关性的准确表示，单词在可视化区域中的传播以提高可视化质量以及在时间范围内的视觉稳定性。我们的方法很灵活，允许用户与显示器互动，以及增量和可扩展性。我们使用丹麦新闻来源的数据显示了三个案例研究的结果。