Semantic interaction for visual text analytics. Endert, A., Fiaux, P., & North, C. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12, pages 473, Austin, Texas, USA, 2012. ACM Press.
Semantic interaction for visual text analytics [link]Paper  doi  abstract   bibtex   
Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts’ mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user’s feedback into account.
@inproceedings{endert_semantic_2012-1,
	address = {Austin, Texas, USA},
	title = {Semantic interaction for visual text analytics},
	isbn = {978-1-4503-1015-4},
	url = {http://dl.acm.org/citation.cfm?doid=2207676.2207741},
	doi = {10.1145/2207676.2207741},
	abstract = {Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts’ mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user’s feedback into account.},
	language = {en},
	urldate = {2019-12-09},
	booktitle = {Proceedings of the 2012 {ACM} annual conference on {Human} {Factors} in {Computing} {Systems} - {CHI} '12},
	publisher = {ACM Press},
	author = {Endert, Alex and Fiaux, Patrick and North, Chris},
	year = {2012},
	keywords = {ENCODING - Model, HOW - Probabilistic Models, Semantic Interactions, Textual information, Type of Work - System, WHEN - Real-Time Applications, WHY - Evaluation of Tools and Systems, HOW - Probabilistic Models, WHY - Adaptive Systems / Guidance, Type of Work - System, ENCODING - ModelAdaptive Systems / Guidance, WHY - Evaluation of Tools and Systems},
	pages = {473},
	file = {Endert et al. - 2012 - Semantic interaction for visual text analytics.pdf:C\:\\Users\\conny\\Zotero\\storage\\KDV2YAEB\\Endert et al. - 2012 - Semantic interaction for visual text analytics.pdf:application/pdf}
}
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