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.

Color is a widely used visual channel for encoding data in visualization design. It is important to select the appropriate type of color mapping to better understand the data. While several studies have investigated the effects of colormaps in various types of information visualization, there have been no studies on their effects on network visualization. Thus, in this paper, we investigate the effects of several colormaps in network visualization using node-link diagrams. Specifically, we compare four different single- and multi-hue colormaps for node attributes, and evaluate their effectiveness in terms of task completion time and correctness rate. Our results show that participants complete their tasks significantly faster with blue (single-hue, sequential) as compared to viridis (multi-hue, sequential), RdYlBu (divergent, red-yellow-blue), and jet (rainbow) colormaps. Additionally, the overall correctness rate shows significant differences between colormaps, with viridis being the least error-prone among the colormaps studied.

(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.

In this paper, we present the new method to easily recognize intricate network and cluster by connecting Multidimensional Scaling(MDS) Map and Social Network Graph, and comprehend the feature of each node using Heatmap Visualization. For the sake of this method, this paper used netizen's movie review data. Also, the process is as follow : 1) We calculated the frequency of sentiment word from each movie review. 2) We designed Heatmap Visualization which easily apprehends main emotion of netizens which appear in each movie review. 3) We made location of each node reflect the frequency of sentiment word by designing Sentiment-Movie Network made up of combining MDS Map and Socal Network Graph, as well as we embodied Network Graph to make same nodes form cluster. 4) By granting meaning in accordance with characteristic of clustering, we applied asterism graphic to facilitate cognitive interpretation. Our demonstration is available to http://idlab.ajou.ac.kr/cosmovis/.