Happy or not: Generating topic-based emotional heatmaps for culturomics using CyberGIS
|Happy or not: Generating topic-based emotional heatmaps for culturomics using CyberGIS|
|Author(s)||Shook E., Leetaru K., Cao G., Padmanabhan A., Wang S.|
|Published in||2012 IEEE 8th International Conference on E-Science, e-Science 2012|
|Keyword(s)||CyberGIS, Digital HASS, Heatmap, Sentiment mining, Spatial text mining (Extra: Analytical approach, Armed conflict, Big datum, Culturomics, Cyber infrastructures, CyberGIS, Digital HASS, Geo coding, Geo-spatial, Geographic contexts, Heatmap, Heatmaps, Human society, Kernel Density Estimation, Multiple attributes, Spatial analysis, Spatial interpolation, Spatial resolution, Text mining, Text-based data, Textual data, Visual analytics, Wikipedia, Population statistics, Supercomputers, Data mining)|
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Happy or not: Generating topic-based emotional heatmaps for culturomics using CyberGIS is a 2012 conference paper written in English by Shook E., Leetaru K., Cao G., Padmanabhan A., Wang S. and published in 2012 IEEE 8th International Conference on E-Science, e-Science 2012.
The field of Culturomics exploits "big data" to explore human society at population scale. Culturomics increasingly needs to consider geographic contexts and, thus, this research develops a geospatial visual analytical approach that transforms vast amounts of textual data into emotional heatmaps with fine-grained spatial resolution. Fulltext geocoding and sentiment mining extract locations and latent "tone" from text-based data, which are combined with spatial analysis methods - kernel density estimation and spatial interpolation - to generate heatmaps that capture the interplay of location, topic, and tone toward narrative impacts. To demonstrate the effectiveness of the approach, the complete English edition of Wikipedia is processed using a supercomputer to extract all locations and tone associated with the year of 2003. An emotional heatmap ofWikipedia's discussion of "armed conflict" for that year is created using the spatial analysis methods. Unlike previous research, our approach is designed for exploratory spatial analysis of topics in text archives by incorporating multiple attributes including the prominence of each location mentioned in the text, the density of a topic at each location compared to other topics, and the tone of the topics of interest into a single analysis. The generation of such fine-grained emotional heatmaps is computationally intensive particularly when accounting for the multiple attributes at fine scales. Therefore a CyberGIS platform based on national cyberinfrastructure in the United States is used to enable the computationally intensive visual analytics.
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