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Context Stamp - A Topic-based Content Abstraction for Visual Concordance
Analysis
VinhTuan Thai, Siegfried Handschuh, Stefan Decker
Digital Enterprise Research Institute, National University of Ireland, Galway
firstname.lastname@deri.org
Abstract
Concordance analysis supports users in studying how
terms are used in a document vs. another by
investigating their usage contexts. This type of analysis
is useful in many domains, from literary study to market
analysis. However, as current approaches usually
present a large set of contexts in their full text form or
as a large frequency-based word cloud, they still
require a lot of effort from users to make sense of the
underlying complex and dynamic semantic dimensions
of contexts. To address this limitation, we propose
Context Stamp as a visual representation of the gist of a
term's usage contexts. To abstract away the textual
details and yet retain the core facets of a term's contexts
for visualization, we blend a statistical topic modeling
method with a combination of the treemaps and Seesoft
visual metaphors. This paper provides a high level
description of the text analysis method and outlines the
visual design of Context Stamps.
1. Introduction
Apart from the needs to search for and navigate to
relevant information, many knowledge workers also
need to compare different usages of a word in one
document vs. another, or at one point in time vs.
another. This comparison can be achieved by looking
into the usage contexts of the word. In literature
analysis, a concordance is commonly used, which
includes an index of the terms in question, their
frequencies and the surrounding contexts. Concordance
analysis “is intended for understanding properties of
language or for analyzing the structure and content of a
document for its own sake, rather than search” [2]. It
helps users investigate word frequencies, study how
terms are used, or which words tend to go well together.
Outside of the literature domain, concordance analysis
can also be used for other purposes. For instance, in
market analysis, it can be used to track how customers'
responses to a product evolve over time.
2. Research Goal
The inherent categorical nature of text and its very
high dimensionality makes it very challenging to
display the contexts graphically [2]. Our goal is to
propose a novel approach to make it easy for users to
quickly compare the sets of contexts within which a
term is used in one document vs another.
Here we consider:
•Instead of presenting a term's set of contexts in their
original textual form, can the details be abstracted away
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and only their gist retained to let users make contextual
sense of the term?
•Which visualization elements can be used together
to convey both the distribution of a term and its
contexts at different levels of detail?
3. Text Analysis and Visual Design
As with other visual analytic solutions, our focus is
not to propose a new visual metaphor, but to identify a
good automated algorithm for the analysis task, and
then integrate the results with appropriate visualization
and interaction techniques.
To abstract away textual details of a set of contexts
and yet retain facets of rich information about them, we
rely on statistical topic models to obtain the relationship
between the mental representation of language
(meaning) and its manifestation in written form. Instead
of treating documents as bags of words, a topic model
treats documents as mixtures of latent topics, and each
topic is a probability distribution over words. A word
can be assigned to various topics with different
probabilities, depending on its levels of association with
various strands of meaning. With a topic model, we
have the inferred distributions of topics within
documents, and the distributions of words over the
topics. The key outcomes are the compositions of the
inferred topics, which are coherent clusters of
thematically related words. While the inferred model is
imperfect as finding the optimal model parameters for a
dataset is non-trivial, these topic-word distributions can
be employed to abstract away textual details of a term's
set of contexts in a document.
To show both the distributions of a term and the
compositions of the core elements of its contexts, we
propose a visualization that is an innovative
combination of the treemaps metaphor [3] and the
Seesoft-based visualization [1].
Further details are available in [4].
4. References
[1] S. G. Eick. Graphically displaying text. Journal of
Computational & Graphical Statistics, 3:127–142, 1994.
[2] M. A. Hearst. Search User Interfaces. Cambridge
University Press, New York, NY, USA, 2009.
[3] B. Shneiderman. Tree visualization with tree-maps: 2-d
space-filling approach. ACM Transactions on Graphics,
11:92–99, January 1992.
[4] V. Thai, S. Handschuh. Context Stamp - A Topic-based
Content Abstraction for Visual Concordance Analysis. In
Proceedings of the 29th of the international conference
extended abstracts on Human factors in computing systems
(CHI 2011), ACM, 2011.