Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams

Chen, Siming; Andrienko, Natalia; Andrienko, Gennady; Li, Jie; Yuan, Xiaoru

doi:10.1109/tvcg.2020.3030411

Cited by 13 publications

(5 citation statements)

References 58 publications

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“…Then, the Paper View is used to view the images of the cases that have ultimately been extracted. It can be seen that ''bridge (CAA20 57 ), pipe (LWW13 58 ), and wheel (ASG21 59 )'' are frequently used, as shown in (4) in Figure 17.…”

Section: User Scenariomentioning

confidence: 99%

“…The visual metaphors mediated by ''Timeline Analysis'' are shown in Figure 19. In the metaphors, accumulation and continuity-which are the subjects of analysis related to time-can be seen to appear Using sewing work (GGS12TPS 69 ), the construction of bridges (CAA20 57 ), and the operation of spacetime tunnels (FAKM15 70 ), ''process metaphors'' are employed as representations to visualize opinions or evaluations exchanged during a specific period. Further, the accumulation of time is shown by arranging or stacking geometric figures such as hexagons (SCS16 71 ), colored dots (SCS17, 72 BN11B 73 ), and lines by section (CSL16 62 ).…”

Section: Interpreting Associated Visual Metaphors After Selecting Int...mentioning

confidence: 99%

“…The visual metaphor for ''Comparison of Different Sentiments'' uses performance opinion holders as the target, as shown in Figure 21. In the distribution of representations, the thicknesses of rivers (MDJW07, 82 XWS16, 83 GYS14, 67 YSK14 61 ), bubble (MEV16, 20 RSRY14, 21 PC15PV, 80 JX17 18 ), and bridges (CAA20 57 ) are considered to examine the differences in the amount of information contained. The shapes of the petal (XD99, 63 CSL12 16 ) and the wheel (a type of structure) (GGS12ST, 69 LGX16, 84 YFS10 85 ) are used to compare the polarities of the comments on the original post by the opinion holder.…”

Section: Interpreting Associated Visual Metaphors After Selecting Int...mentioning

confidence: 99%

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An exploration system to effectively analyze the visual metaphor used in sentiment visualization

Ha,

Moon,

Joung

et al. 2024

Information Visualization

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In the field of data visualization, there has been a recent trend of using a complex type of visualization with a multidimensional structure or using several visualizations in parallel when summarizing the results of sentiment analysis. Although this trend may be useful for sophisticated sentiment analysis, such analysis is difficult for the general public and novice researchers. To address this issue, there has recently been a trend of visualizing sentiments using visual metaphors. To facilitate the understanding of related cases, it is necessary to have a systematic means of grasping the sentiment target, the purpose and motivation of research, and the representations used as substitutes for visual metaphors. Therefore, the objective of the present study was to develop an exploration system that can analyze the visual metaphors used in the case of sentiment visualization. For this study, (1) sentiment visualization cases in which visual metaphors are used were collected. (2) After a taxonomy composed of the categories of “target, intermediation, representation, visual variable, and visualization technique” was constructed, it was used to analyze sentences of visual metaphors appearing in sentiment visualization cases. (3) An exploration system capable of grasping the semantic relationships of sub-elements within the five categories of the taxonomy and intuitively interpreting visual metaphors was developed so that appropriate cases can be recommended to sentiment visualization researchers. (4) The approach and usefulness of the exploration system were explained using user scenarios. (5) A case study was conducted to show that the provided system can be analyzed from various perspectives. (6) The usability of the exploration system was demonstrated through a verification targeting experts. The proposed system allows researchers and analysts to intuitively grasp “what types of visual metaphor method and idea should be equipped to visualize sentiment data in an easier way to understand.”

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Section: User Scenariomentioning

confidence: 99%

Section: Interpreting Associated Visual Metaphors After Selecting Int...mentioning

confidence: 99%

Section: Interpreting Associated Visual Metaphors After Selecting Int...mentioning

confidence: 99%

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An exploration system to effectively analyze the visual metaphor used in sentiment visualization

Ha,

Moon,

Joung

et al. 2024

Information Visualization

View full text Add to dashboard Cite

show abstract

“…An example of marker placement is shown in Figure 4, bottom. The tool enabling placement of markers on an image was earlier developed for conducting user evaluations of visualisations [CAA*21].…”

Section: Our Experimentsmentioning

confidence: 99%

Seeking Patterns of Visual Pattern Discovery for Knowledge Building

Andrienko

Chen

Fisher

2022

Computer Graphics Forum

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Currently, the methodological and technical developments in visual analytics, as well as the existing theories, are not sufficiently grounded by empirical studies that can provide an understanding of the processes of visual data analysis, analytical reasoning and derivation of new knowledge by humans. We conducted an exploratory empirical study in which participants analysed complex and data‐rich visualisations by detecting salient visual patterns, translating them into conceptual information structures and reasoning about those structures to construct an overall understanding of the analysis subject. Eye tracking and voice recording were used to capture this process. We analysed how the data we had collected match several existing theoretical models intended to describe visualisation‐supported reasoning, knowledge building, decision making or use and development of mental models. We found that none of these theoretical models alone is sufficient for describing the processes of visual analysis and knowledge generation that we observed in our experiments, whereas a combination of three particular models could be apposite. We also pondered whether empirical studies like ours can be used to derive implications and recommendations for possible ways to support users of visual analytics systems. Our approaches to designing and conducting the experiments and analysing the empirical data were appropriate to the goals of the study and can be recommended for use in other empirical studies in visual analytics.

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“…Ajani et al [1] used a rating scale from "very hideous" to "very beautiful" in their study on the aesthetics of three visualization designs. Chen et al [22] used a rating scale from "nice" to "ugly" to study the aesthetic appearance of visualization technique. These examples target what we call aesthetic pleasure but are mostly based on intuition rather than a verified instrument that can ascertain that the terms indeed measure the aesthetic pleasure of visualizations reliably and validly.…”

Section: Aesthetic Pleasure In Visualizationmentioning

confidence: 99%

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

He,

Isenberg,

Dachselt

et al. 2022

Preprint

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We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies. Given the lack of an aesthetic pleasure scale dedicated to visualizations, researchers have mostly chosen their own terms to study or compare the aesthetic pleasure of visualizations. Yet, many terms are possible and currently no clear guidance on their effectiveness regarding the judgment of aesthetic pleasure exists. To solve this problem, we engaged in a multi-step research process to develop the first validated rating scale specifically for judging the aesthetic pleasure of a visualization (osf.io/fxs76). Our final BeauVis scale consists of five items, "enjoyable," "likable," "pleasing," "nice," and "appealing." Beyond this scale itself, we contribute (a) a systematic review of the terms used in past research to capture aesthetics, (b) an investigation with visualization experts who suggested terms to use for judging the aesthetic pleasure of a visualization, and (c) a confirmatory survey in which we used our terms to study the aesthetic pleasure of a set of 3 visualizations.

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Co-Bridges: Pair-wise Visual Connection and Comparison for Multi-item Data Streams

Cited by 13 publications

References 58 publications

An exploration system to effectively analyze the visual metaphor used in sentiment visualization

An exploration system to effectively analyze the visual metaphor used in sentiment visualization

Seeking Patterns of Visual Pattern Discovery for Knowledge Building

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations

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