This paper suggests a method for refining a massive amount of collective intelligence data and visualizing it with a multilevel sentiment network in order to understand the relevant information in an intuitive and semantic way. This semantic interpretation method minimizes network learning in the system as a fixed network topology only exists as a guideline to help users understand. Furthermore, it does not need to discover every single node to understand the characteristics of each clustering within the network. After extracting and analyzing the sentiment words from the movie review data, we designed a movie network based on the similarities between the words. The network formed in this way will appear as a multilevel sentiment network visualization after the following three steps: (1) design a heatmap visualization to effectively discover the main emotions on each movie review; (2) create a two-dimensional multidimensional scaling (MDS) map of semantic word data to facilitate semantic understanding of network and then fix the movie network topology on the map; (3) create an asterism graphic with emotions to allow users to easily interpret node groups with similar sentiment words. The research also presents a virtual scenario about how our network visualization can be used as a movie recommendation system. We next evaluated our progress to determine whether it would improve user cognition for multilevel analysis experience compared to the existing network system. Results showed that our method provided improved user experience in terms of cognition. Thus, it is appropriate as an alternative method for semantic understanding.

(1) Background: The Electronic Medical Record system, which is a digital medical record management architecture, is critical for reliable medical research. It facilitates the investigation of disease patterns and efficient treatment via collaboration with data scientists. (2) Methods: In this study, we present multidimensional visual tools for the analysis of multidimensional datasets via a combination of 3-dimensional radial coordinate visualization (3D RadVis) and many-objective optimization (e.g., Parallel Coordinates). Also, we propose a user-driven research design to facilitate visualization. We followed a design process to (1) understand the demands of domain experts, (2) define the problems based on relevant works, (3) design visualization, (4) implement visualization, and (5) enable qualitative evaluation by domain experts. (3) Results: This study provides clinical insight into dementia based on EMR data via visual analysis. Results of a case study based on questionnaires surveying daily living activities indicated that daily behaviors influenced the progression of dementia. (4) Conclusions: This study provides a visual analytical tool to support cluster segmentation. Using this tool, we segmented dementia patients into clusters and interpreted the behavioral patterns of each group. This study contributes to biomedical data interpretation based on a visual approach.

A logo is an effective way of expressing a brand’s identity and an essential element in conveying the values and image of the company. The development process of a competitive logo should be based on a design that is future-proof in a rapidly changing global market; hence, understanding the design trends for successful logo design is key. In this study, the design shape elements of logo trend models were analyzed and made into a database. Then, a trend analysis system was produced using radial visualization (RadViz) and circular parallel coordinates data visualization techniques. RadViz allows observation of clusters of logos that have similar shape elements, whereas with circular parallel coordinates plots, detailed information of the shape elements of each logo trend can be seen. Using the system, it was confirmed that shape elements—such as transformation to surface, overlapping, artificiality, concept of color and rhythm—play a major role in driving a trend. It was observed that trends change over time as various shape elements are added or removed. In addition, our study is expected to help predict the logo trend models that will come into style in the future. While similar efforts have been made in the past, our proposed system improves upon them by utilizing standard design elements as the categorizing criteria, using a unique combination of RadViz and circular parallel coordinates data visualization techniques. Using our system as a guideline, many users would be able to create logos that reflect what is trending.