Task-based Evaluation of Sentiment Visualization Techniques

Kostiantyn Kucher, Kostiantyn Kucher, Samir Bouchama, Samir Bouchama, Achim Ebert, Andreas Kerren, Andreas Kerren

2022

Abstract

Sentiment visualization is concerned with visual representation of sentiments, emotions, opinions, and stances typically detected in textual data, for example, charts or diagrams representing negative and positive opinions in online customer reviews or Twitter discussions. Such approaches have been applied for the purposes of academic research and practical applications in the past years. But the question of usability of these various techniques still remains generally unsolved, as the existing research typically addresses individual design alternatives for a particular technique implementation only. This work focuses on evaluation of the effectiveness and efficiency of common visual representations for low-level visualization tasks in the context of sentiment visualization. More specifically, we describe a task-based within-subject user study for various tasks, carried out as an online survey and taking the task completion time and error rate into account for most questions. The study involved 50 participants, and we present and discuss their responses and free-form comments. The results provide evidence of strengths and weaknesses of particular representations and visual variables with respect to different tasks, as well as specific user preferences, in the context of sentiment visualization.

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Paper Citation


in Harvard Style

Kucher K., Bouchama S., Ebert A. and Kerren A. (2022). Task-based Evaluation of Sentiment Visualization Techniques. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, ISBN 978-989-758-555-5, pages 187-194. DOI: 10.5220/0010916400003124


in Bibtex Style

@conference{ivapp22,
author={Kostiantyn Kucher and Samir Bouchama and Achim Ebert and Andreas Kerren},
title={Task-based Evaluation of Sentiment Visualization Techniques},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,},
year={2022},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010916400003124},
isbn={978-989-758-555-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP,
TI - Task-based Evaluation of Sentiment Visualization Techniques
SN - 978-989-758-555-5
AU - Kucher K.
AU - Bouchama S.
AU - Ebert A.
AU - Kerren A.
PY - 2022
SP - 187
EP - 194
DO - 10.5220/0010916400003124