%0 Conference Paper %1 conf/ieeevast/LiuWCZG14 %A Liu, Shixia %A Wang, Xiting %A Chen, Jianfei %A Zhu, Jim %A Guo, Baining %B IEEE VAST %D 2014 %E Chen, Min %E Ebert, David S. %E North, Chris %I IEEE Computer Society %K correlated-topic-modeling graph-based topic-modeling visualization %P 183-192 %T TopicPanorama: A full picture of relevant topics. %U https://ieeexplore.ieee.org/abstract/document/7042494 %X We present a visual analytics approach to developing a full picture of relevant topics discussed in multiple sources such as news, blogs, or micro-blogs. The full picture consists of a number of common topics among multiple sources as well as distinctive topics. The key idea behind our approach is to jointly match the topics extracted from each source together in order to interactively and effectively analyze common and distinctive topics. We start by modeling each textual corpus as a topic graph. These graphs are then matched together with a consistent graph matching method. Next, we develop an LOD-based visualization for better understanding and analysis of the matched graph. The major feature of this visualization is that it combines a radially stacked tree visualization with a density-based graph visualization to facilitate the examination of the matched topic graph from multiple perspectives. To compensate for the deficiency of the graph matching algorithm and meet different users' needs, we allow users to interactively modify the graph matching result. We have applied our approach to various data including news, tweets, and blog data. Qualitative evaluation and a real-world case study with domain experts demonstrate the promise of our approach, especially in support of analyzing a topic-graph-based full picture at different levels of detail. %@ 978-1-4799-6227-3

@inproceedings{conf/ieeevast/LiuWCZG14, abstract = {We present a visual analytics approach to developing a full picture of relevant topics discussed in multiple sources such as news, blogs, or micro-blogs. The full picture consists of a number of common topics among multiple sources as well as distinctive topics. The key idea behind our approach is to jointly match the topics extracted from each source together in order to interactively and effectively analyze common and distinctive topics. We start by modeling each textual corpus as a topic graph. These graphs are then matched together with a consistent graph matching method. Next, we develop an LOD-based visualization for better understanding and analysis of the matched graph. The major feature of this visualization is that it combines a radially stacked tree visualization with a density-based graph visualization to facilitate the examination of the matched topic graph from multiple perspectives. To compensate for the deficiency of the graph matching algorithm and meet different users' needs, we allow users to interactively modify the graph matching result. We have applied our approach to various data including news, tweets, and blog data. Qualitative evaluation and a real-world case study with domain experts demonstrate the promise of our approach, especially in support of analyzing a topic-graph-based full picture at different levels of detail.}, added-at = {2020-09-22T10:35:31.000+0200}, author = {Liu, Shixia and Wang, Xiting and Chen, Jianfei and Zhu, Jim and Guo, Baining}, biburl = {https://www.bibsonomy.org/bibtex/23d0cead22dbb58581198d99d4ffa3e34/ghagerer}, booktitle = {IEEE VAST}, crossref = {conf/ieeevast/2014}, editor = {Chen, Min and Ebert, David S. and North, Chris}, ee = {http://doi.ieeecomputersociety.org/10.1109/VAST.2014.7042494}, interhash = {922ff358d9f0657b4521f8bc86dbc746}, intrahash = {3d0cead22dbb58581198d99d4ffa3e34}, isbn = {978-1-4799-6227-3}, keywords = {correlated-topic-modeling graph-based topic-modeling visualization}, pages = {183-192}, publisher = {IEEE Computer Society}, timestamp = {2020-09-22T10:35:31.000+0200}, title = {TopicPanorama: A full picture of relevant topics.}, url = {https://ieeexplore.ieee.org/abstract/document/7042494}, year = 2014 }