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Morphable Word Clouds for Time-Varying Text Data Visualization

机译：可变形的文字云，用于随时间变化的文本数据可视化

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摘要

A word cloud is a visual representation of a collection of text documents that uses various font sizes, colors, and spaces to arrange and depict significant words. The majority of previous studies on time-varying word clouds focuses on layout optimization and temporal trend visualization. However, they do not fully consider the spatial shapes and temporal motions of word clouds, which are important factors for attracting people’s attention and are also important cues for human visual systems in capturing information from time-varying text data. This paper presents a novel method that uses rigid body dynamics to arrange multi-temporal word-tags in a specific shape sequence under various constraints. Each word-tag is regarded as a rigid body in dynamics. With the aid of geometric, aesthetic, and temporal coherence constraints, the proposed method can generate a temporally morphable word cloud that not only arranges word-tags in their corresponding shapes but also smoothly transforms the shapes of word clouds over time, thus yielding a pleasing time-varying visualization. Using the proposed frame-by-frame and morphable word clouds, people can observe the overall story of a time-varying text data from the shape transition, and people can also observe the details from the word clouds in frames. Experimental results on various data demonstrate the feasibility and flexibility of the proposed method in morphable word cloud generation. In addition, an application that uses the proposed word clouds in a simulated exhibition demonstrates the usefulness of the proposed method.
机译：词云是文本文档集合的直观表示，该文本文档使用各种字体大小，颜色和空格来排列和描绘重要的单词。先前有关时变词云的大多数研究都集中在布局优化和时间趋势可视化上。但是，他们没有充分考虑词云的空间形状和时间运动，这是吸引人们注意力的重要因素，也是人类视觉系统从时变文本数据中捕获信息的重要线索。本文提出了一种新颖的方法，该方法利用刚体动力学在各种约束下以特定形状序列排列多时间单词标签。每个单词标签都被视为动力学的刚体。借助于几何，美学和时间相干性约束，所提出的方法可以生成时间可变形的词云，该词云不仅将单词标签排列成它们相应的形状，而且随着时间的流逝平滑地变换了词云的形状，从而产生了令人愉悦的随时间变化的可视化。使用提议的逐帧和可变形词云，人们可以从形状过渡观察时变文本数据的整体故事，并且人们还可以从框架中的词云观察细节。在各种数据上的实验结果证明了该方法在可变形词云生成中的可行性和灵活性。此外，在模拟展览中使用提议的词云的应用程序演示了提议的方法的有用性。

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