We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to the audiences. To inform the design, we first reviewed online examples of animated texts and animated wordles, and summarized strategies for injecting emotion into the animations. We introduced a composite approach that extends an existing animation scheme for one word to multiple words in a wordle with two global factors: the randomness of text animation (entropy) and the animation speed (speed). To create an emordle, general users can choose one predefined animated scheme that matches the intended emotion class and fine-tune the emotion intensity with the two parameters. We designed proof-of-concept emordle examples for four basic emotion classes, namely happiness, sadness, anger, and fear. We conducted two controlled crowdsourcing studies to evaluate our approach. The first study confirmed that people generally agreed on the conveyed emotions from well-crafted animations, and the second one demonstrated that our identified factors helped fine-tune the delivered emotion extent. We also invited general users to create emordles on their own based on our proposed framework. Through this user study, we confirmed the effectiveness of the approach. We concluded with implications for future research opportunities of supporting emotion expression in visualizations.

翻译：我们提出了一种名为emordle的概念设计，通过动画化紧凑词云（wordle）向观众传递情感语境。为支撑该设计，我们首先回顾了动画文本与动画词云的在线示例，归纳出将情感注入动画的策略。我们引入了一种复合方法，通过两个全局因素（文本动画的随机性（熵）和动画速度（速度））将针对单个词汇的现有动画方案扩展至词云中的多词场景。普通用户可通过选择与目标情感类别匹配的预定义动画方案，并利用这两个参数微调情感强度来创建emordle。我们针对快乐、悲伤、愤怒与恐惧四种基本情感类别设计了概念验证型emordle示例。通过两项受控众包研究评估该方案：第一项研究证实人们普遍能感知精良动画传递的情感，第二项研究表明我们识别的参数有助于微调情感表达程度。我们还邀请普通用户基于所提框架自主创建emordle，通过用户研究验证了方法的有效性。最后，我们总结了未来在可视化中支持情感表达的研究机遇及其启示。