Geometry-Based Edge Clustering for Graph Visualization
IEEE Transactions on Visualization and Computer Graphics
Web Science 2.0: Identifying Trends through Semantic Social Network Analysis
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
User-directed sentiment analysis: visualizing the affective content of documents
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Twitinfo: aggregating and visualizing microblogs for event exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multilevel agglomerative edge bundling for visualizing large graphs
PACIFICVIS '11 Proceedings of the 2011 IEEE Pacific Visualization Symposium
Graph Bundling by Kernel Density Estimation
Computer Graphics Forum
Force-directed edge bundling for graph visualization
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Sentiment analysis for tracking breaking events: a case study on twitter
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Micro-blog sentiment analysis attracts much attention by companies, governments and other organizations. It could help companies to estimate the extent of product acceptance and to determine marketing strategies, governments to monitor online public perception and to improve government-public relation, etc. Researchers mainly focused on time-varying analysis or space varying analysis. This paper combines time-varying analysis and space varying analysis and proposes an Electron Cloud Model (ECM) based on the Schrodinger equation and Niels Bohr atomic theory to conduct time-varying visual analysis of micro-blog sentiments. In the ECM, an attempt to map a score of sentiment to the electron stability is made. Kernel density estimation and edge bundling are used to conduct space-varying visual analysis of sentiments. The former visualizes sentiment changes in different levels of detail naturally while the latter can reduce visual clutter of edge crossing and reveal high-level edge pattern.