1 Vector Space Model – 2 Visual Interactive Interfaces.
Avoiding tedious backbox model refinement through interactive semantic interfaces!
This visual analytics technique is designed to enable domain knowledge externalization.
It supports four tasks, enabling users to:
A continuous quality monitoring and refinement recommendation supports these tasks and enables targeted user guidance.
Supporting Two Views based on the same Vector Space.
Base Words are all words that are neither part of the higher levels of the concept, nor of the topic hierarchies. They can be promoted to become keyword and/or descriptors through user interaction. On the other hand, demoted keywords and/or descriptors traverse down the hierarchy to become base words.
Linked through Spatial Positioning.
The Semantic Concept Space is designed as two stacks of layered, interactive canvases comprising the two views of the visual interface:
Both views are separate, super-positioned canvases. Each view is composed of three layers, representing its hierarchy levels above the base words.
Users can interact with one view at a time, while the other is toggled inactive. To facilitate comparison between views, the inactive view is shown with a low opacity in the background of the active one, making its elements shine through the canvas.
Modeling the Semantic Concept Space.
Mapping the Concept and Topic Layers.
Our visual workspace is designed to support: (1) finding different elements on the canvas; (2) decoding the type of word object at hand; and (3) analyzing the spatial association of words.
For each hiearchy layer we represent each word object by default with a label and enable users to toggle on a circle as an additional marker. Both the circle and the label sizes encode the object level in the data hierarchy.
For the topic view, we designed a topic glyph that represents the topic or document association with different concept regions. This glyph can be used as another alternative representation for the object marks on the canvas layers.
The Machine Teaching Window.
An example initial concept view based on the 2012 US Presidential Debates between Romney and Obama. In this example, the left side of the concept view represents a region on renewable energy (bottom) and terrorism (top).
The Machine Learning Window.
In the corresponding topic view, the topic on on oil production is placed to renewable energy and the topic on a terror attack in Libya between the two concepts as it is related to both.
Interactive Learning from the User’s Semantics.
The iterative refinement of a topic modelling output is achieved indirectly through concept refinement. Users can trigger an update to the t-SNE projection, as well as the topic modeling, at any time during this process.
Users have two options for Concept Refinement:
Both options can be used anytime throughout the visual analytics process to adjust the concept hierarchy.
Topic Modeling Adaptation
The topic modeling view can not be adjusted directly but is used for inspecting and analyzing the topic modeling result. Only through recomputing the topic modeling algorithm (on-demand) do the layers of the topic modeling change to adapt to the concept refinements.
This duality of views enables users to teach the machine learning model their domain knowledge, as well as the machine learning model to respond through learning the new semantics.