The Story of One Clement, Plaisant, Vuillemot


The Story of One: Humanity scholarship with visualization and text analysis
Tanya Clement*, Catherine Plaisant#, Romain Vuillemot#
*Dept. of English; #Human-Computer Interaction Laboratory
University of Maryland, College Park, USA
tclement@umd.edu; plaisant@cs.umd.edu; romain.vuillemot@insa-lyon.fr

Most critiques of The Making of Americas (Paris 1925) by Gertrude Stein contend that
the text deconstructs the role narrative plays in determining identity by using indeterminacy to
challenge readerly subjectivity. The current perception of Making as a postmodern text relies on
the notion that there is a tension created by frustrated expectations that result from the text’s
progressive disbandment of story and plot as the narrative unweaves into seemingly chaotic,
meaningless rounds of repetitive words and phrases. Yet, a new perspective that is facilitated by
digital tools and based on the highly structured nature of the text suggests that these instabilities
can be resolved by the same seemingly nonsensical, non-narrative structures. Seeing the manner
in which the structure of the text makes meaning in conversation with narrative alleviates
perceived instabilities in the discourse. The discourse about identity formation is engaged—not
dissolved in indeterminacy—to the extent that the reader can read the composition.
One method for reading the composition of the text without relying on what becomes a
non-existent framework based on plot is to view the progression of words according to a
different framework, a framework that relies on comparative associations based on word usage.
Using WordHoard,i we compared word usage between texts and text parts by calculating the
log-likelihood ratio, which describes the size and significance of the difference between word
frequencies in a base text versus a reference text.ii In this analysis, we measured word usage in
The Making of Americans in comparison to two different sets of reference texts with more
traditional narrative structures: (a) a set of 19th century novels written by Jane Austen, Charles
Dickens, George Eliot, and George Meredith; iii (b) between the first and second half of Making,
which it has been argued also represents different narrative trends (Clement 2008). Visualizing
this information in Wordleiv —a word cloud application (Wattenberg & Viegas, 2008)—is useful
primarily because it provides a visual overview of word frequencies that is easy to understand
and to publish for reference. The Wordle application facilitates this kind of analysis by
visualizing the list of words in a cloud that maximizes the space utilization on a computer screen
by sizing the words by their relative frequencies. The more frequently a word occurs in a
particular text (relative to another text) the larger the word appears. In the set of visualizations
that accompany this discussion, v each cloud serves to visualize words that are more or less
frequent in any given comparison (see examples in Fig. 1). What becomes immediately evident
in comparing these visualizations is the prominence of a particular word that consistently scores
a high value in terms of discrepancy between Making and the reference texts: one.
The word one appears consistently across every cloud that marks the words that are more
common in Making and less common in the sample of nineteenth century texts and words that
are more common in the second half rather than the first half of the text. We then compared the
relative frequencies of multiple pronouns, which revealed that the frequency of one surges by the
end of the text (Fig. 2). What is most interesting about this graph is that the high frequency of
one is the result of the confusion accomplished by the word’s schizophrenic nature. Words here
are represented according to occurrence, not to type of occurrence. Thus, the word one —unlike

The Story of One Clement, Plaisant, Vuillemot

he, she, I, we, or even you or it—which plays many positions in the text, in the role of a pronoun
or an adjective and in the subject or object position, surges in frequency.
To better understand how the word one is used in the text, we created another set of
visualizations prepared using a prototype we developed called PosViz.vi PosViz allowed us to
compare word usage based on parts of speech in individual chapters from Making to the whole
text. These comparisons allowed us to isolate and analyze words used more and less frequently
throughout the course of the novel and to measure how and if these patterns change within the
text itself. In these visualizations, each part of speech for each word is treated as a separate word
instance and each instance is placed according to its appearance in the text, color-coded
according to its part of speech, and sized according to its overall frequency. Thus, by using
PosViz, the progression of the manner in which the word one is used in terms of different parts of
speech is documented, allowing us to see that the use of one appears to change as the text
progresses. For example, a relatively small one appears three times in the chapter 1 cloud (Fig.
3). This visualization indicates that the occurrence of the word one has little variance in terms of
how it is used (its part of speech) and occurs relatively infrequently in occurrences that are
localized to the beginning paragraphs of the chapter.vii By chapter 9, however, one dominates the
discourse both in terms of its frequency and in terms of its multiple uses (Fig. 4).
By identifying the manner in which word usage changes in correlation to the presence and
absence of narrative both in comparison to other novels and within the text itself, these
comparisons enable a new perspective on the meaning-making processes of the text’s
composition. For example, these visualizations illustrate the nature of the word one as it is used
to heighten the word’s propensity for different reading possibilities. This lends to a reading in
which one may represent a singular subject position or multiple subject positions at once. With
this information, a further argument can be made that the discourse about identity formation is
engaged in this multiplicity, not dissolved in indeterminacy. Thus, employing composition in her
representation of identity formation in The Making of Americans becomes the method by which
Stein seeks to represent identity, but if and how the reader is able to recognize and interpret this
endeavor is predicated by her ability to see it.
This work with The Making of Americans is part of research and development within the
MONK (Metadata Offer New Knowledge) project, a Mellon-funded collaborative seeking to
develop text mining and visualization software in order to explore patterns across large-scale text
collections. Stein’s text was a productive text for analysis during the beginning phases of the
MONK project since its many and complicated repetitions could be processed and visualized.viii
This presentation focuses on how the process of determining decision criteria for text mining led
to the discovery that various textual features (n-grams, parts-of-speech, and log-likelihood ratios)
and various visualizations (FeatureLens, Spotfire, Wordle, and PosViz) ultimately facilitated an
iterative discovery process and a new reading of Gertrude Stein’s The Making of Americans.

Appendix:

The Story of One Clement, Plaisant, Vuillemot

A B
Fig. 1: Comparsions between Making and novels by George Meredith, using log-likelihood ratios from
WordHoard and visualizations produced in Wordle; A: words that are more common in Meredith; B: words
that are more common in Making.


“one”
“he”
“I”
“she”
“they”
“you”
Section 4
“we”
Section 1 Section 2

Section 3 Section 5
Fig. 2: The frequency of “one,” “I,” “you,” “we,” “he,” “she,” and “they” are mapped across the five sections
of the text in Spotfire

The Story of One Clement, Plaisant, Vuillemot



Fig. 3: Chapter 1, the word “one” highlighted in PosViz visualization


Fig. 4: Chapter 9, the word “one” highlighted in PosViz visualization

The Story of One Clement, Plaisant, Vuillemot


Notes
i Please see http://wordhoard.northwestern.edu/userman/.
ii This analysis is based on Dunning’s log-likelihood analysis. Please see
http://wordhoard.northwestern.edu/userman/analysis-comparewords.html#loglike.
iii The books used in this study are those available in the WordHoard application. They are listed
at http://terpconnect.umd.edu/~tclement/less-more.htm. These authors were chosen because
Stein repeatedly compares her text to their novels. See Stein 1990, p. 506.
iv Please see http://wordle.net/.
v These visualizations are pictured in an online appendix entitled “Visual Comparisons of
Gertrude Stein’s The Making of Americans using WordHoard, Wordle, and PosViz” at
http://terpconnect.umd.edu/~tclement/less-more.htm. In these slides, the comparisons have been
visualized with four sets of data for each data set, all of which are set in comparison to the base
text The Making of Americans and include and exclude ‘common words’ such as articles,
conjunctions, and pronouns. The full list of these ‘stop-words’ is unavailable, but the creator
Jonathan Feinberg has indicated in an email that ‘I have modified them by hand over time. The
English one came from the Snowball stemmer project’ at
http://snowball.tartarus.org/algorithms/english/stop.txt (personal correspondence).
vi Currently, PosViz does not have a web presence.
vii The analysis program used to label these uses is part of the SEASR (Software Environment for
the Advancement of Scholarly Research) analytic routines (http://seasr.org/). Though imperfect,
the system is consistent—it labels the same behaviors the same way each time. Thus each
occurrence represents the perception of a different use of the word one.
viii This work is published in two articles: Don et al., 2007 and Clement, 2008.
References
Clement, T. (2008). “‘A thing not beginning or ending’: Using Digital Tools to Distant-Read
Gertrude Stein's The Making of Americans.” Literary and Linguistic Computing, 23.3: 361-382.
Don, A., Zheleva, E., Gregory, M., Tarkan, S., Auvil, L., Clement, T., Shneiderman, B.,
Plaisant, C. (2007). “Discovering interesting usage patterns in text collections: integrating text
mining with visualization.” Proceedings of the sixteenth ACM conference on Conference on
information and knowledge management, pp. 213-222.
Stein, G. (1990). “Transatlantic Interview 1946.” In The Gender of Modernism. Bonnie Kime
Scott and Mary Lynn Broe (eds). Bloomington: Indiana University Press, pp. 502-516.
Wattenberg, M. and Viegas, F. (2008). “Tag clouds and the case for vernacular visualization.”
Interactions, 15.4: 49-52.
Stein, G. (1995). The Making of Americans: Being a History of a Family's Progress. Normal, IL:
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Weiss, Sholom M. et al. (2005). Text Mining: Predictive Methods for Analyzing Unstructured
Information. New York: Springer, pp. 85-86.