@article {DOI10.1109/TVCG.2012.260,
	title = {Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {18},
	year = {2012},
	month = {12/2012},
	pages = {2879 - 2888},
	abstract = {Visual analytic tools aim to support the cognitively demanding task of sensemaking. Their success often depends on the ability to leverage capabilities of mathematical models, visualization, and human intuition through flexible, usable, and expressive interactions. Spatially clustering data is one effective metaphor for users to explore similarity and relationships between information, adjusting the weighting of dimensions or characteristics of the dataset to observe the change in the spatial layout. Semantic interaction is an approach to user interaction in such spatializations that couples these parametric modifications of the clustering model with users{\textquoteright} analytic operations on the data (e.g., direct document movement in the spatialization, highlighting text, search, etc.). In this paper, we present results of a user study exploring the ability of semantic interaction in a visual analytic prototype, ForceSPIRE, to support sensemaking. We found that semantic interaction captures the analytical reasoning of the user through keyword weighting, and aids the user in co-creating a spatialization based on the user{\textquoteright}s reasoning and intuition.},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2012.260},
	author = {Endert, Alex and Fiaux, Patrick and North, Chris}
}
@conference {Endert:2012:SIV:2207676.2207741,
	title = {Semantic interaction for visual text analytics},
	booktitle = {Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems},
	series = {CHI {\textquoteright}12},
	year = {2012},
	pages = {473{\textendash}482},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	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{\textquoteright} 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{\textquoteright}s feedback into account.},
	keywords = {interaction, Visual Analytics, visualization},
	isbn = {978-1-4503-1015-4},
	doi = {10.1145/2207676.2207741},
	url = {http://doi.acm.org/10.1145/2207676.2207741},
	author = {Endert, Alex and Fiaux, Patrick and North, Chris}
}
