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Authors: Kostiantyn Kucher 1 ; 2 ; Samir Bouchama 3 ; 1 ; Achim Ebert 3 and Andreas Kerren 1 ; 2

Affiliations: 1 Department of Computer Science and Media Technology, Linnaeus University, Växjö, Sweden ; 2 Department of Science and Technology, Linköping University, Norrköping, Sweden ; 3 Computer Graphics and HCI Group, Technical University of Kaiserslautern, Kaiserslautern, Germany

Keyword(s): Sentiment Visualization, Sentiment Analysis, Visual Variable, Visual Representation, Visual Encoding, User Study, Text Visualization, Visual Analytics, Information Visualization.

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 stu dy 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. (More)

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Paper citation in several formats:
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 (VISIGRAPP 2022) - IVAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 187-194. DOI: 10.5220/0010916400003124

@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 (VISIGRAPP 2022) - IVAPP},
year={2022},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010916400003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - IVAPP
TI - Task-based Evaluation of Sentiment Visualization Techniques
SN - 978-989-758-555-5
IS - 2184-4321
AU - Kucher, K.
AU - Bouchama, S.
AU - Ebert, A.
AU - Kerren, A.
PY - 2022
SP - 187
EP - 194
DO - 10.5220/0010916400003124
PB - SciTePress