Contributions to Proceedings:
A. Iiijima, T. Itoh, H. Wu, N. Grossmann:
"Visualization of Semantic Differential Studies with a Large Number of Images, Participants and Attributes";
in: "Proceedings of the 24th International Conference on Information Visualisation (iV2020)",
R. Francese (ed.);
issued by: IEEE Press;
IEEE C.P.S. Publishing Services,
2020,
1
- 8.
English abstract:
he Semantic Differential (SD) Method is a rating scale to measure the semantics. Attributes of SD are constructed by collecting the responses of participantīs impres-sions of the objects expressed through Likert scales representing multiple contrasting with some adjective pairs, for example, dark and bright, formal and casual, etc. Impression evaluation can be used as an index that reflects a human subjective feelings to some extent. Impression evaluations using the SD method consist of the responses of many participants, and therefore, the individual differences in the impressions of the participants greatly affect the content of the data. In this study, we propose a visualization system to analyze three aspects of SD, objects (images), participants, and attributes defined by adjective pairs. We visualize the impression evaluation data by applying dimension reduction so that, users can discover the trends and outliers of the data, such as images that are hard to judge or participants that act unpredictably. The system firstly visualizes the attributes or color distribution of the images by applying a dimensional reduction method to the impression or RGB values of each image. Then, our approach displays the average and median of each attribute near the images. This way, we can visualize the three aspects of objects, participants and attributes on a single screen and observe the relationships between image features and user impressions /attribute space. We introduce visualization examples of our system with the dataset inviting 21 participants who performed impression evaluations with 300 clothing images.
Keywords:
Semantic Differential Method, Visualization
Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_291362.pdf
Created from the Publication Database of the Vienna University of Technology.