The Craig Zeta Spreadsheet

1
The Craig Zeta Spreadsheet
Hoover, David L.
david.hoover@nyu.edu
New York University, USA
Zeta, a new measure of textual difference
introduced by John F. Burrows, can be used
effectively in authorship attribution and stylistic
studies to locate an author's characteristic
vocabulary–"marker" words that one author
uses consistently, but another author or authors
use much less frequently or not at all
(Burrows 2005, 2006; Hoover 2007a, 2007b,
2008). Zeta analysis excludes the extremely
common words that have traditionally been
the focus and concentrates on the middle
of the word frequency spectrum. Beginning
in 2008, I developed an Excel spreadsheet
implementation of Zeta and its related measure
Iota (which focuses on words that are relatively
rare in one author, but extremely rare or
absent from others), available on my Excel
Text-Analysis Pages. Recently, however, Craig
has developed an alternative version of Zeta
that simultaneously creates sets of marker
words and anti-marker words and has applied
it impressively to Shakespeare authorship
problems (Craig and Kinney, 2009; Hoover,
forthcoming). Although Craig's Zeta focuses on
the same part of the word frequency spectrum
as Zeta, its calculation and results are quite
different. Because it seems poised to become
an important tool for computational stylistics,
I have created and will demonstrate the Craig
Zeta Excel spreadsheet that automates its
calculation.
Craig's Zeta is a powerful but simple method
of measuring differences among authors. It
begins with two sets of texts divided into
about equal-sized sections, then compares how
many sections for each author contain each
word, ignoring the frequencies of the words
and concentrating on their consistency of
appearance. The most natural comparison is
between two authors, but it can be used to
study any contrast. My demo version contrasts
thirteen female and thirteen male American
poets born between 1911 and 1943 (about 8,000
words of poetry by each, divided into two
sections).
The heart of the method is that it combines the
ratio of the sections by one author in which each
word occurs with the ratio of the sections by
the other author from which it absent into a
single measure of distinctiveness for each word.
Zeta scores theoretically range from two (for a
word found in every section by one author and
absent from every section by the other), to zero
(for a word found in no sections by one author
and in all sections by the other). Sorting the
words on this composite score produces two lists
of words, one favored by the first author and
avoided by the second, the other favored by the
second author and avoided by the first.
The snippet from the spreadsheet in Fig. 1
(shown before the macro operates) will clarify
the calculation. In E7 and E8, the user enters
labels (automatically copied into columns A
and G and Row 9) for the two groups to be
compared. The data to be analyzed is in columns
H through CA, rows 11ff, with most columns
minimized so the various categories are visible.
The combined word frequency list for the two
groups is in column G, with the raw frequencies
for each word in each section in columns H-
CA. The calculation is performed in columns A-
E. Column D sums the sections of poetry by
women that contain the word, and column E
sums the sections of poetry by men that do
not contain the word. The most frequent words
typically occur in all sections, but note that
me
,
the 30
th
most frequent word, is absent from
one of the men's sections. Column B calculates
the ratio of women's sections containing the
word to the total number of women's sections;
column C calculates the ratio of men's sections
not containing the word to the total number
men's sections. Column A sums columns B and
C to produce the Zeta scores. Columns H-CA
of row 1 show the number of different words
(word types) in each section, and below them,
the percentage of types that are marker words
for women or men (these are not meaningful
until the macro has operated). In cells F2-F3
the user can set the number of marker words
for each group at different levels for sections of
different sizes and can see three sets of results at
once in H-CA.

2
Figure 1
An Excel macro automates the calculation
of Zeta. Word frequency lists for the texts
to be analyzed are entered into five sub-
sheets (not shown). One contains the sections
of the primary group and one contains the
sections of the secondary group. Two more sub-
sheets contain any independent sections by the
primary and secondary groups (these can be
used to test the method's success on known
texts). Finally, there is a sub-sheet for the texts
to be tested. The macro clears out old data,
enters formulas into columns H-CA, rows 2-7,
copies the texts out of the sub-sheets into the
main sheet, shrinks the columns, and enters
ranks for the words in column F. It then sorts
the words on their Zeta scores in column A,
descending, so that the words most distinctively
used by women appear at the top and those most
distinctively used by men are at the bottom. It
selects the 1000 most distinctive men's words
and resorts them in reverse order, with the
most distinctive at the top of their section.
The sheet can handle 15,000 words, but the
sample above analyzes 14,000 words (calculated
in cell B2), so rows 11-1010 will contain the
1000 most distinctive women's words and rows
13,011-14,010 the 1000 most distinctive men's
words.
Figure 2 shows the data after the macro runs.
Here
mother's
, found in 13 of 26 sections by
women and absent from 24 of 26 sections by
men, is the most distinctive women's word in
this comparison. The most distinctive men's
word,
cross
, is found in just 3 of 26 sections
by women, but is absent from only 10 of 26
sections by men. The most distinctive words
for each group are found in columns CB and
CC in descending order of distinctiveness. The
figures in rows 2-7 of columns H-CA show how
these distinctive words are distributed in each
section. For example, H2-H3 shows that almost
15% of the words Derricote uses in this section
are among the 500 most distinctive women's
words, but only about 5% are among the 500
most distinctive men'swords. For Ammons (1),
in AH2-AH3, the first section by a man, the
proportions are roughly reversed.
Figure 2
Columns BH-CA contain the same information
for the test sections, 25 sections of poetry by
seven female and seven male poets whose work
had no part in the creation of the word list on
which the analysis is based. Finally, the macro
creates a scatter graph of the analysis with
section names as labels (Fig. 3). The vertical axis
records the proportion of types in each text that
are marker words for men and the horizontal
axis does the same for marker words for women.
As Fig. 3 shows, twenty of the twenty-five
new sections of poetry (80%) trend toward the
group that matches their gender. The fact that
these same words produce a similar, though
slightly less accurate, result for seven male and
seven female contemporary novelists is further
evidence that the method is capturing some kind
of genuine difference. This is a strong result,
especially given the relatively small samples
and the wide diversity that characterizes 20th
century American poetry. The little chart that
follows Fig. 3 shows some of the clusters of
related words that occur in the women's and
men's marker words, and hints at the kinds of
analysis that Craig's Zeta makes possible.
Figure 3

3
Cluster
500 Women's
Markers
500 Men's Markers
Family
mother's, father's,
mother, father,
children, ancestral,
aunt, baby, birth,
child, child's, cousins,
daughters, family,
generations, uncles
Religion
altar
,
nuns
, and
praying
faith, heaven, hell,
prayers, souls, spirit,
Christ, gods, myth,
paradise, religion,
spirits, temple
Houses/
Furniture
danced
song, dancing, sing,
dance, sang, dancer,
music, singer, singing,
sings
Personal
Pronouns
he'll, I'd, mine,
ourselves, she'd, she's,
they'd, you'd, you're,
yourself
References
Burrows, J. F.
(2006). 'All the Way Through:
Testing for Authorship in Different Frequency
Strata'.
LLC.
22
: 27-47.
Burrows, J. F.
(2005). 'Who wrote Shamela?
Verifying the Authorship of a Parodic Text'.
LLC.
20
: 437-450.
Craig, H., and Kinney, A., eds.
(2009).
Shakespeare, Computers, and the Mystery of
Authorship.
Cambridge: Cambridge University
Press.
Hoover, D.
(2007a). 'Corpus Stylistics,
Stylometry, and the Styles of Henry James'.
Style.
41
: 174-203.
Hoover, D.
(2007b). 'Quantitative Analysis
and Literary Studies'.
A Companion to Digital
Literary Studies.
Susan Schreibman, Ray
Siemens (eds.). Oxford: Blackwell, pp. 517-33.
Hoover, D.
(2008). 'Searching for Style
in Modern American Poetry'.
Directions in
Empirical Literary Studies: Essays in Honor of
Willie van Peer.
Sonia Zyngier, et. al. (eds.).
Amsterdam: John Benjamins, pp. 211-27.
Hoover, D.
(2009). 'The Excel Text-Analysis
Pages'.
https://files.nyu.edu/dh3/public/The
%20Excel%20Text-Analysis%20Pages.html
.
Hoover, D.
(2010: forthcoming). 'Authorial
Style'.