ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

Wang, Yunhai; Lee, Bongshin; Chu, Xiaowei; Zhang, Kaiyi; Bao, Changchun; Li, Xiaotong; Zhang, Jian; Fu, Chi‐Wing; Hurter, Christophe; Deußen, Oliver

doi:10.1109/tvcg.2019.2934783

Cited by 12 publications

(6 citation statements)

References 33 publications

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“…To encode extra information, most studies took advantage of the natural visual channels of wordles. For instance, the neighboring regions were used to indicate semantically relevant words [22], [79], [80], while the wordle contour also implies the theme of the content [11], [73]. DancingWord [62] leveraged the movement of wordle to imply a change of scene in storytelling [62].…”

Section: Wordle Variantsmentioning

confidence: 99%

“…We then assigned the font size and position of words with the same rank number derived from a randomly generated number. While some wordles leverage particular shapes (e.g., [11], [73]) to convey extra information, we adopted the rectangular layout in the controlled study to reduce the impact of any relevant factors. All stimuli shared the same canvas size and shape.…”

Section: Set-upmentioning

confidence: 99%

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Creating Emordle: Animating Word Cloud for Emotion Expression

Xie

Shu

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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We propose emordle, a conceptual design that animates wordles (compact word clouds) to deliver their emotional context to 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 extent of the emotion delivered. We also invited general users to create their own emordles 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.

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Section: Wordle Variantsmentioning

confidence: 99%

Section: Set-upmentioning

confidence: 99%

Creating Emordle: Animating Word Cloud for Emotion Expression

Xie

Shu

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…Our work is also related to word or tag clouds generation [2,10,11,13,17,18]. Word clouds usually are used for visualizing text, which adjusts font sizes, colors, and spatial locations to ill words into a given canvas.…”

Section: Related Workmentioning

confidence: 99%

“…Combining metro map and wordle, Li et al [11] proposed a visual design method of keywords extracted from POI (point of interest) data to assist users to retrieve and choose a POI destination eiciently. In addition, Wang et al [18] presented the creation of shape-bounded wordles, called ShapeWordle, by utilizing a diferential shape-aware form of Archimedean spirals to guide word placement within a shape. All the above works focus on how to visualize the text information eiciently, using a compact and non-warped word cloud.…”

Section: Related Workmentioning

confidence: 99%

Creating Word Paintings Jointly Considering Semantics, Attention, and Aesthetics

Zhang

Yang

Jin

et al. 2022

ACM Trans. Appl. Percept.

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In this paper, we present a content-aware method for generating a word painting. Word painting is a composite artwork made from the assemblage of words extracted from a given text, which carries similar semantics and visual features to a given source image. However, word painting, usually created by skilled artists, involves tedious manual processes, especially when generating streamlines and laying out text. Hence, we provide an easy method to create word paintings for users. How to design textural layout that simultaneously conveys the input image and enables easy access to the semantic theme, is the key challenge to generate a visually pleasing word painting. To address this issue, given an image and its content related text, we first decompose the input image into several regions and approximate each region with a smooth vector field. At the same time, by analyzing the input text, we extract some weighted keywords as the graphic elements. Then, to measure the likelihood of positions in the input image that attract the observers’ attention, we generate a saliency map with our trained visual attention model. Finally, jointly considering visual attention and aesthetic rules, we propose an energy-based optimization framework to arrange extracted keywords into the decomposed regions and synthesize a word painting. Experimental results and user studies show that this method is able to generate a fashionable and appealing word painting.

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“…Classic word clouds are often generated using forced-based approaches, alongside with a spiral placement heuristic [14][15][16] that allows for a very compact final layout. This method is powerful even when the rough position of a word is dictated by an underlying map [4,12].…”

Section: Introductionmentioning

confidence: 99%

Layered Area-Proportional Rectangle Contact Representations

Nöllenburg¹,

Villedieu²,

Wulms³

2021

Preprint

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We investigate two optimization problems on area-proportional rectangle contact representations for layered, embedded planar graphs. The vertices are represented as interior-disjoint unit-height rectangles of prescribed widths, grouped in one row per layer, and each edge is ideally realized as a rectangle contact of positive length. Such rectangle contact representations find applications in semantic word or tag cloud visualizations, where a collection of words is displayed such that pairs of semantically related words are close to each other. In this paper, we want to maximize the number of realized rectangle contacts or minimize the overall area of the rectangle contact representation, while avoiding any false adjacencies. We present a network flow model for area minimization, a linear-time algorithm for contact maximization of two-layer graphs, and an ILP model for maximizing contacts of k-layer graphs.

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ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals

Cited by 12 publications

References 33 publications

Creating Emordle: Animating Word Cloud for Emotion Expression

Creating Emordle: Animating Word Cloud for Emotion Expression

Creating Word Paintings Jointly Considering Semantics, Attention, and Aesthetics

Layered Area-Proportional Rectangle Contact Representations

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