Creating Emordle: Animating Word Cloud for Emotion Expression



Bad Blood




One Art



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.

Keywords —— Wordle, Animation, Affective Visualization, Authoring, Casual Visualization


Liwenhan Xie, Xinhuan Shu, Jeon Cheol Su, Yun Wang, Siming Chen, and Huamin Qu. 2023. Creating Emordle: Animating Word Cloud for Emotion Expression. IEEE Transactions on Visualization and Computer Graphics. Early Access. DOI: 10.1109/TVCG.2023.3286392


@article {xie2023emordle,
    title = {Creating Emordle: Animating Word Cloud for Emotion Expression},
    author = {Liwenhan Xie and Xinhuan Shu and Jeon Cheol Su and Yun Wang and Siming Chen and Huamin Qu},
    journal = {IEEE Transactions on Visualization and Computer Graphics},
    year = {2023},
    doi = {10.1109/TVCG.2023.3286392},
    url = {},
    publisher = {IEEE},
    note={Early Access}