MLA 7
th
1
Team Research Proposal
Team POLITIC
Political Opinions in Literature: Identifying Themes in International Compositions
Robert Cai, Matthew Carr, Adam Elrafei, Alexander Goniprow,
Adrian Hamins
-
Puertolas, Manpreet Khural, Andrew Li, Alexandra Winter,
Soumya Yanamandra, Dan Yang, and Kay Zhang
University of Maryland Gemstone Program
Mentor: Dr. Peter Mallios
Librarian: Timothy Hackman
and
The Maryland Institute for Technology in the Humanities
We pledge on our honor that we have not given or received
any unauthorized assistance on this
assignment.
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2
Introduction
The United States
was
involved in numerous interna
tional conflicts throughout
the 20
th
century. A prevalent theory suggests deeper public understanding of foreign cultures might have
allowed
th
e United States to avoid several
of these conflicts, including the Iran Hostage C
risis
and the Vietnam War (Li). Since the United States is a democracy,
citizen
perception of foreign
countries has a direct relationship with foreign policies enacted.
A thorough
understanding
of
how the
American
public gathers its perceptions of foreign cultures is crucial
to
fully
comprehend
American foreign policy and
international relations
. Foreign literature is one
important medium that exposes the United States to
the political and cultural ideologies of other
countries (Griswold 1077). The
American
public
reads
novels
by foreign authors
to
gain
an
intimate
p
erspective of
foreign
societies
—
view
s
unavailable
through
domestic media.
Readers
can
also
connect to other
cultures because n
ovels create emotional ties by appealing to universal
human themes (Aubry 27). At the same time, international and domestic political concerns guide
the
United
States’
public interest in foreign literature. For instance, it is not a coinc
idence that the
peaceful
writings of Gandhi became important in the United States during the Civil Rights
Movement
(Mallios 10
-
19).
H
owever,
different
foreign
authors
often provide
opposing viewpoints of
their societies
.
The most popular wor
ks
form a selective base
of foreign literature that potentially accommodates
elites’
self
-
serving political biases.
Using experimental methods, Gilens asserts
that the
United
States’
ignorance and misinformation “leads many [citizens] to hold political views
different
from those they would hold otherwise” (379).
Therefore, understanding public intent and attitude
requires knowing why certain novels
and authors
seem representative of a cultural canon
. To
become a better
-
informed political citizen of the
United
States, one must
think critically about
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3
the uses of foreign literature.
Our
study
will
investigate how publicly available United States media
r
eceived
for
eign
novels
and authors and how these portrayals work
toward social and political ends of
government support and criticism
(Mallios 10
-
19). Specifically, we will conduct a low
-
constraint
case study of Russian literature to address the following question: Did
the
reception of Russian
novels and authors
in the United States
and United States fore
ign policy toward Russia r
eflect
each other from 1900
-
1923
? We hypothesize
that
the reception of Russian literature
in the
United States
significantly correlates with United States policies toward Russia
, due to inherent
ties between literary evaluation
and political understanding
. Scholars,
politicians
, and other
government officials will likely take
interest in our study.
We
will use the portrayals of
selected
Russian novels
and authors
in nationally available
print media to
define the reception
of Russian literature
in the United States during this time
period
. We recognize scholars could
investigate how
alternative fo
rms of media, such as pictures
or political cartoons
,
influence public understanding
.
However, we chose print media because it
is
the easiest to quantitatively analyze.
We will define United Stat
es foreign policy toward
Russia
through quantifiable measures such as foreign aid, military investment
, and trade deals
from 1900
-
1923
. This will take the form of overarching topics that
describe the types of policies
enacted, such as interventionism and humanitarianism. Our analysis will include keyword
searches relative to both literary reception and foreign policy. We will track how these themes
have evolved over time using techniques o
f topic
modeling.
1
Our study does not seek to determine a relationship between
political climates and
messages found in novels, opinions held by authors, o
r motivations behind translators. Instead,
we will determine the extent to which
there is a
relationship between media reception of Russian
1
See Appendix H for an example of topic modeling output.
Team POLITIC
4
literature
in the United States
and the political climate.
Our
research is distinguished from
previous studies in two wa
ys: it analyzes reception in
United States media and not the intent of
authors or transl
ators, and we will accomplish our a
nalysis through quantitative,
not
just
qualitative
,
methods.
Throughout the rest of our proposal, we will summarize our literature review, outline our
methodology, explain the limitations of our research, list confoundi
ng variables, and conclude
with descriptions of our anticipated results, our budget, our timeline, and the statistical tools we
will use throughout the project.
Literature Review
Introduction of Russian Literature in the Western World
Eugene
-
Melchoir de Vogue's
Le Roman Russe (The Russian Novel)
in 1886 represented
the increasing interest in Russian literature in Weste
rn Europe and America. Many
writers,
including Isabel Hapgood and Constance Garnett
,
published English translations of R
ussian
novels, short stories, and poems to critical acclaim
in subsequent decades
(Mose
r
431
). In other
words, the late nine
tee
nth and early
twentie
th centuries
mark
ed the availability of Ru
ssian
literature to US
public and intellectuals.
Many studies
have sought to understand
literary themes found in major Russian works.
For example, Emerson analyzes Leo Tolstoy’s views on war through a close reading of his many
texts (1855). However, only a few studies address Russian literary reception in
the United
States
during the early twentieth century
. One of these rarities is Goldfarb’s account of how a
prominent literary critic, William Dean Howells, suppo
rted Tolstoy’s works in the United States
during the twentieth century
(318). However, this study is limit
ed in that it only contemplates
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5
Russian literary reception through Howells’ and his critics’ views. We intend to expand on such
studies by using comprehensive statistical tools to analyze a wide
r base
of reception material.
Canon
Formation
and Politics
Political motivations shape a nation’s literary canon, which in turn projects that nation’s
identity.
The idea of a national literature emerged in the late eighteenth century as a way of
proving cultural independence on an international level (Corse,
Natio
nalism and Literature
7
-
14).
Original research studies suggest canonical or high
-
culture literature does not reveal how
citizens perceive themselves, but rather how elites want
to envision their nation (ibid
74).
These
previous studies turn to college syll
abi and literary prizes to define the most frequently appearing
works as canonical or high
-
culture (Brown, 1; Corse,
Nations and Novels
1279
-
82). Unlike
bestsellers or popular culture novels, canonical texts differ greatly between countries, as they are
sy
mbolic in value and not simply “economic commodities
.
”
Theories of canon formation state
novels
have to
experience a conjunction of large sales and certain types of recognition
to reach
canonical status
(Ohmann 206). This recognition refers to the critical
reception of works found in
publications that “carried special weight in forming cultural judgments
,
” such as the
New York
Times Book Review
and the
New Republic
(204).
However, scholars have never specified the
ways i
n which elites have translated
cross
-
cultural
differences into literature.
Topic Modeling
Researchers use topic modeling to analyze large corpora
of d
ata. Topic modeling affirms
“documents are mixtures of topics, where a topic is a probability distribution over wor
ds”
(Steyvers 2). Fur
thermore, Latent Dirichlet A
llocation (LDA), a more specific type
of topic
modeling, asserts
each document from a larger corpus consist
s of a plurality of topics (Chaney
and Blei
2). In past studies,
researchers have used
topic modeling in general and LDA
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6
specifically to analyze large corpora of data. For example, a 100
-
topic LDA model generate
d
word probabilities under each topic for all articles in the journal
Science
between 1880 and 2002
(ibid
4).
More complex versions of topic modeling, however,
can
g
ather more inf
ormation from
our Russian
author database
.
For example, Topics over Time (TOT) models
can
account for the
chronology
of documents in a corpus (ibid
9).
Since our documents are dynamic in that they
change over time,
LDA
would
confound the topi
cs’ changes and lose
any pe
rceivable patterns
.
Xuerui
Wang and Andrew McCallum explain the topic analysis of US
Presidential State
-
of
-
the
-
Union
addresses, where LDA
“confounds Mexican
-
American War (1846
-
1848) with some
aspects of World War I (1914
-
1918)” s
ince it is “unaware of the 70
-
year separatio
n between the
two events” (1). Modeling topics over time
serves to address this issue.
In
Wang and McCallum’s study, they incorporated
timestamps
to help
track “changes in
the occurrence of
the
topics
themselves” as a function of time
(2)
. They tested their model on
three data sets: “more than two centuries of U.S. Presidential State
-
of
-
the
-
Union addresses,” “17
-
year history of the NIPS [Neural Information Processing Systems] conference,” an
d “nine
mont
hs of email archive
”
(ibid).
The r
esults of their study show
the TOT model is able to predict
the
timestamps of documents and
generates topics that are “more distinct from each other than
LDA topics” (
ibid
5). In our research, we will also u
se a TOT
model
on the databases we
anticipate constructing
to
account for time.
Furthermore, modified
versions of LDA can relate metadata to topics. Meta
data is
information about the documents we collect such as “author, title, geograp
hi
c location, [and]
links” (Blei
10)
. Therefore, we can also correlate influences s
uch as the gender and ethnicity
of
the authors of the reception material to word probabilities found in
topics
in our corpus
.
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7
Sentiment Analysis
S
entiment analysis
is also useful for sorting through large corpora of data
. While topic
modeling focuses on the subject of the data in question, sentiment analysis focuses on the
opinion expressed about the
subject matter of the data (Lee
and Pang 1). Multiple methods can
determine
the sentiment of a piece of data.
Lee and Pang
compared three different algorithms
used for sentiment analysis: the Naive Bayes, maximum entropy classification
,
and support
vector machines (
ibid
3). The Nai
ve Bayes algorithm is a
simplistic algor
ithm
. It
may not hold to
high accuracy rates with complicated sets of data, but it “tends to perform surprisingly well” and
is even the ideal algorithm for use with “problem classes with highly dependent features” (
ibid
).
Maximum entropy classification and
support vector machines are both much more sophisticated
methods. Maximum entropy classification algorithms “make no assumptions about the
relationships between features”, which will make it better than Naive Bayes with data that has
little or no dependen
ce on similar features (
ibid
4). Support vector machines differ from both of
the previous methods in that they do not focus on probability, which brings them much closer to
traditional methods used for normal topic modeling adapted to work with sentiment a
nalysis
(
ibid
4).
For our projec
t,
sentiment analysis methods will allow us to
quickly categorize articles by
gauging how American periodicals perceive and discuss Russian authors and novels during the
time period of interest
. In addition, incorporating a sentiment categorization into
our database
will allow future researchers
to quickly add to and examine our data
.
Foreign Policy Analysis
Political scientists have devised several models and theories to explain how foreign
p
olicy develops (Boyer 185). One such theory is the rational actor model, which
states
stimuli
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8
and immediate responses lead to the creation of foreign policy (Boyer 189). However, the
political aspect of our study does not seek to determine how political le
aders create foreign
policy, but rather
attempts to measure and quantify it. Many previous studies have determined
United States foreign policy towards various nations by analyzing its components. For example,
Rick Travis analyzes foreign policy towards Af
rica by focusing on foreign aid to the continent
(798). Haslam focuses on direct foreign investment and the corresponding treaties to determine
United States foreign polic
y toward other nations (1182).
For our study
, we will gather data on
“exports, import
s, investments, arms sales, and categories of foreign aid (bilateral, aggregate, and
per capita)” between the United States and the Russian Empire (and later the Soviet Union) to
define United States foreign policy (Watson 253).
Methodology
Our first task
s
were
to determine a
time
range and
country
to investigate
, as outlined in
the literature review
.
We
selected
an upper
time
bound
of 1923,
since
all preceding publications
are
in the public domain
and
we can publicly release
all collect
ed data
.
We chose
1
900
as our
lower time bound
to guarantee
a significant number of periodicals
will be available
.
2
Time
allowing, we may be able to expand the t
ime period of interest
, guaranteeing more articles for
analysis.
We
decided to investigate Russian literature
for several reasons
.
First
,
Russia was a
focal point of the United States
during the twentieth century
.
World War I, the Bolshevik
Revolution, and the threat of communism led to increased
public and governmental
interest in
Russia
during our
selected time period
.
Second,
only
a
relatively small
number of significant
Russian authors
had works
available in English
at the time
.
A narrow range of Russian literary
figures sugg
ests
American
periodicals interested in examining Russian literature
had
to
invoke
2
We anticipate finding a significant number of periodicals referencing Russian literary figures during the selected
time period, as shown in Appendix E. By the beginning of our time period of interest, many national periodicals had
already been well establi
shed (Baldasty).
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9
certai
n Russian literary figures
and works
frequently
, leading to
larger sample size
s
for the
selected authors
.
Subsequently,
we
will be able to construct a more exhaustive corpus
3
of
Russian l
iterature than o
f
the
more readily available
lite
rature from other countries, such as
Britain or France.
To decide which
literary figures
to study,
we
compiled a list Russian
literary figures
whose works had
English
translations during our time period of interest
.
Using that list,
we
cataloged
the number of sear
ch results found in the Readers’
Guide Retrospective
4
for each
literary figure
of interest.
5
From this
preliminary
summary of the availability of
periodicals
in the
United States
specifically discussing Russian literary figures,
we
chose to inve
stigate Dostoevsky
and Tolstoy
to
maintain the feasibility of our
study.
We bring
some bias
in our selection of
literary figures, as
we have
chosen
two of the most
re
nowned Russian
liter
ary figures in the
United States
. Therefore,
our
data re
garding the reception of selected Russian literary figures in
the United States will not be representative of th
e entirety of Russian literary figures
.
We could
add one or two minor
R
ussian authors to our research
to increase the external validity of our
p
roject
if time permits
.
We
resolved to capture a large, representative sample of the body of articles
that
explicitly mention
our
selected Russian literary figures
in periodicals
popular
in the United
States
between 1900 and
1923.
We
will construct a database containing
these
articles
using the
Reader
s’ Guide Retrospective ind
ex.
The Retrospective’s emphasis on more popular periodicals
fits well with
our intent
to gain
an
understanding of how the
general
American
public perceived
significant Russian
literary figures
in the early twentieth century
. We
will use a
subject search of
3
See our Glossary of Terms in Appendix H
4
The Readers’ Guide Retrospective is a comprehensive index of 608 popular periodicals published in the United
States spanning from 1890 to 1982. 224 periodicals in the Readers’ Guide Retrospectiv
e
–
almost 37% of the
database
–
are available prior to 1923. See Appendix G.
5
An abridged version of this list can be found in Appendix E.
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10
se
lected
literary authors
to
explore th
e Reader
s
’
Guide Retrospective and find articles
appropriate for the constructed database.
Scanni
ng
Since
most articles in the Rea
ders’
Guide are not digitized,
we
have to
digitize
the
physical or micr
ofilm versions of articles that
fall within search parameters. We are currently
scanning a
rticles
by
using publicly available resources at the Universi
ty of Maryland McKeldin
Library. Therefore, our
initial database construction wil
l
contain only
articles
available within
the University of Maryland archive system. Should time permit, it may be feasible to explore
other academic archive
s for articles from
the Readers’
Guide Retrospective
.
We have standardized s
canning techniques to reduce preventable variat
ions in image
quality and size.
6
Systematic
errors, including the
presence of
dust par
ticles, stains, and other
debris
on the scanning glass,
also
contribute to poor
image quality
and complicate analysis of the
database
.
We will
therefore
wipe down the scanning glass
with glass cleaner solution and a
microfiber cloth before and after
each
scan to
reduce this source of error.
P
reservation of the scann
ed material is essential to
data accuracy and reliability
. During
microfilm scanning, an auto
-
adjust function adjusts the brightness and
scanned size
of
each page
to produce an optimally clear image. Furthermore,
we must adjust
the resolution
of the scanne
r
up from the default 300 dots per inch (DPI) to t
he maximum setting of 600 DPI.
Similar settings
are also present on the non
-
print source scanners.
O
nce saved, the file is left unmodified with the
exception of cropping. We
will
not manipulate
images
after scanning
to re
tain the original image
data,
quality
, and integrity
.
6
Examples of standardization in scanning articles include: uniform Scanner type, Scanner settings, and format in
w
hich material is saved. Images will be saved in the Tag Image File Format , a standard “for distributing high
quality scanned images or finished photographic files” (“TIFF Files”).
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11
We will convert these files to readable documents through Optical Character Recognition
software.
We are using ABBYY FineReader
11 to save the files as plain text documents, DjVu
f
iles, and FineReader documents. Topic modeling and sentiment analysis software can analyze
plain text files; the DjVu format compresses documents and maintains the layout of text on each
page; and we save FineReader files to document the transition from sc
anned image to readable
text.
At this stage, we
remove pictures from the
pages.
Foreign Policy Analysis
The second portion of the project focuses on United States foreign policy toward Russia.
Our goal is
to
quantify
the United States’ changing attitude and foreign policy towards Russian
over
the
established time period for the study of the authors. As mentioned earlier on, one
method of
defining this relationship
is
to examine
statistical data that relates to foreign
policy
including
foreign aid
to Russia
, trade relations, and
America’s
military presence in
Russia
.
We
will also examine
Presidential
s
peeches
delivered
dur
ing the time period of interest; we
will
simply
run searches for
references to
Russia
and
transfer
Presidential
speeches
that
produce hits
into a database for future analysis.
With
sufficient time, we will also
collect and analyze
newspaper editorials
in a similar
manner
.
A
theory discovered in preliminary research indicates
that
editorials of major
newspaper
s
of the late nineteenth
and early twentieth centurie
s, specifically
The New York
Times,
reflected political motivations of the United States government (“Deductions” 42;
Lippmann and Merz 3). If pursued,
a newspaper editorial
database
provides our project with a
wider
scope because it provides an additional
level
of comparison
with
other foreign policy data
.
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12
Annotation
As we assemble
a corpus of articles regarding literary authors of interest
, one priority is
to ensure we
effectively organize
the constructed database
. We can more easily analyze an
organized corpus
,
making it
essential
for
generat
ing
metadata
7
. Beyond ease of analysis,
metadata will give
us
the ability to categ
orize and analyze articles that
deal with
a
spec
ific topic
or exhibit similar traits, an approach that will yield more significant and interesting results than a
simple keyword search. The assembled corpus’s metadata will include, at a minimum, historical
and archival data concerning each article.
We
wi
ll also attempt
to capture metadata
regarding the
characteristics of each article, such as whether articles include explicit references to ra
dical
politics, by annotating
8
each article.
Annotation questions may reflect biases and stereotypes that
we
bring
individually
to the
project and it is difficult to ensure
our
uniformity in annotation. We
determined what kind of
metadata to capture and refined annotation questions by annotating a sample of articles
from
the
assembled database.
9
The goal of refining a
nnotation quest
ions is to confirm
we will arrive at
similar answers if annotating independently
.
In conjunction with
the
M
aryland
I
nstitute for
T
echnology in the
H
umanities
(MITH)
,
we
will attempt to automate the process by which
we construct metadata
,
reducing t
ime spent on this
portion of our
methodology. It is feasible to automate metadata collection through computer
scripts, including collection of spelling variations in literary author name
s across the constructed
7
According to the National Information Standards Organization, metadata is
“structured information that describes,
explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource…metadata is often
called data about data” (National Information Standards Organization).
8
Annotation is a way to produce variables that will allow us to understand the political significance of Russian
Literature in the United States and catalog the constructed corpus.
9
Reference to revisions of Annotation Questions in Appendix D.
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13
corpus,
10
or, more abstractly,
p
erfo
rming sentiment analys
is on articles in the corpus.
The end
goal of our
research project is to form conclusions abo
ut the relationship between the
reception
of Russian
l
iterature
in the United States and United States
foreign policies.
To reach
these
conclusions,
we
will need to analyze both an annotated database of articles that pertain to
literature and an annotated database of articles that pertain to foreign policies.
In the data analysis section of the methodology,
we expect
to discover t
rends in the
databases that provide answers to certain questions. For the Russian literature database, the
questi
ons will focus on the discourse
throughout the United States
surrounding the predominant
Russi
an authors
.
11
To
conduct this style of
data analy
sis, we
will use a collection of data mining strategies.
Data mining refers to the process of collecting unknown properties of a database.
Two basic
strategies are keyword frequencies
12
and semantic parsing.
13
The most important data mining analysis
we pl
an to
co
nduct
is probabilistic topic
modeling, “a suite of algorithms that aim to discover and annotate large archives of documents
with thematic information” (Blei 2). A topic is a collection of words that all have a high
probability of being associated t
o
one another
. The basic probabilistic topic modeling is Latent
10
The names
of Russian authors often have a number of accepted spellings and are subject to frequent
mistranslation (Pasterczyk). We will catalog alternative spellings of selected literary figures. The use of Boolean
operators to search for common name variations in
a keyword search of the Readers’ Guide Retrospective will
increase the number of articles found that relate to Russian literary authors of interest. An example of common name
variations can be found in Appendix F.
11
See Appendix C for current annotation gu
idelines.
12
Keyword frequencies, achieved by using the publicly available Text Analysis Portal for Research (TAPoR) tool,
will allow for organization of data on a more general
level (Berson).
An example of the information that TAPor can
provide are the fre
quencies of author references and how often author names are found near each other.
13
We will achieve semantic parsing by using software programs Shalmaneser and FrameNet, developed by the
International Computer Science Institute at the University of Calif
ornia, Berkley
. These programs will allow
us
to
analyze databases using ‘frames,’ which
,
according to FrameNet
,
are semantic representations of situations. These
tools highlight the types of sentences used in specific articles. For example, If an article contains many sentences
framed under the semantic categories of ‘Judgment’ and ‘Assessment,’
we
can safely concl
ude that article contains a
number of opinionated statements. See Appendix H for more information.
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14
Dirichlet Allocation (LD
A), as described in our literature
review. The end result is that
all the
articles in the database will
have labels
with proportions of various topics
, which can then
be
categorized based on topic frequency
.
By comparison,
we will implement
a
supervised version of
LDA
(sLDA)
in the automation of metadata creation.
14
Finally, the last form of topic
modeling
that
we
will use is the Topics Over
Time model (TOT),
described i
n the literature review, which
will
introduce a time variable into our analysis
(Wang and McCallum 5).
At the conclusion of this step in the process,
we will have fully annotated and labeled
the
databases by all the various data mining strategies. From this data,
we
can determine certain
trends in the topics in the articles. It is these trends that will allow
us to make
certain inferences
about the relationship between the reception of Russian
literature
in the United States
and United
States
foreign policy.
Conclusion
Our research aims to provide new insight into how the United States receives
foreign authors and novels and how this reception relates to US foreign policy.
Our anticipated
resu
lts are vital to a recent development in the humanities known as the globalization of
American literary studies, given that “the mechanisms by which [differences between countries]
are translated into literature have never been fully specified” (Corse,
Nat
ions and Novels
1279).
Foreign novels are an inherent part of
United States
culture and if one were to ignore the
presence o
f foreign literature in United States
politics, then one would be ignoring a major factor
that shaped both the citizens and governme
nt of the
United States. “A sound public opinion
cannot exist without access to the news” and “evidence is needed” to reveal inherent biases in
14
In sLDA,
“each document is paired with a response. The goal is to infer latent topics predictive of the response”
(Blei
and McAuliffe 1). Instead of lett
ing the software construct its own distribution over topics,
we
will provide a
fitted model, specifically the annotation form previously mentioned in the methodology (ibid).
Then
the software
can
predict a response for the previously designated topics, suc
h as sentiment, nationality, racism, politics, etc.
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15
publicly available portrayals of political events (
Lippmann and Merz 1
).
Experts in fields of
literary studies c
laim scholars reach “little agreement about what constitutes literary value in this
field” and there exists “unnecessary confusion
as to clear standards and goals
”
in evaluating
these types of
literature
(Brown 1
-
8). We are
also
pioneering relatively new software and
technology in the realm of literary analysis.
By May 9
th
, 2012, we plan to have compiled a sample database of several hundred
articles scanned and processed through the OCR software in preparation for a technical sem
inar
with MITH.
Our
annotation team hopes to annotate
150 of these articles
. The goal of this
seminar is to experiment with some of the
available
database analysis software to determine how
effectively the computer programs can learn to annotate articles i
ndependently and
whether any
trends in the meta
data begin to surface.
We anticipate finding a distinct correlation between the
reception of foreign literature and public attitudes toward foreign policy.
We
will
compile our
completed
findings into an additi
ve online database, to which other scholars can contribute
similar research. Over time, our foundation will pave the way to understanding overall patterns
in foreign literature reception.
Team POLITIC
16
Works Cited
Aubry, Timothy. "Afghanistan Meets the Amazon:
Reading the Kite Runner in America."
PMLA: Publications of the Modern Language Association of America
124.1 (2009): 25
-
43.
EBSCO.
Web. 10 Sept. 2011.
Baldasty, Gerald J.
E.W. Scripps and the Business of Newspapers.
Urbana
-
Champaign: U of
Illinois P,
1999. Print.
Berson, Alex, Stephen Smith, and Kurt Thearling.
Building Data Mining Applications for CRM.
New York: McGraw Hill, (1999): n. pag. Print.
Blei, David M., and Jon D. McAuliffe. “Supervised Topic Models.”
Princeton U and U of
California, Ber
keley, 2010. Web. 17 Mar. 2012.
Blei, David M. “Introduction to Probabilistic Topic Models.”
Communications of the ACM.
Princeton U,
n.d. Web. 17 Mar. 2012.
Boyer, Mark A. "Issue Definition and Two
-
Level Negotiations: An Application to the American
Foreign Policy Process."
Diplomacy & Statecraft
11.2 (2000): 185
-
212.
America: History
and Life with Full Text
. Web. 27 Nov. 2011.
Brown, Joan L., and Crista Johnson. "Required Reading: The Canon in Spanish and Spanish
American Literature."
Hispania
81.
1 (1998): 1
-
19.
JSTOR.
Web. 12 Sept. 2011.
Chaney, Allison J.B., and David M. Blei.
“Visualizing Topic Models.”
International AAAI
Conference on Social Media and Weblogs.
Princeton U
Dept. of Computer Science,
2012. Web. 15 Mar. 2012.
Corse, Sarah M.
Nationalism and Literature: The Politics of Culture in Canada and the United
States
. Cambridge: Cambridge University Press, 1997. Print.
Team POLITIC
17
---
. "Nations and Novels: Cultural Politics and Literary Use."
Social Forces
73.4 (1995): 1279
-
308.
JSTOR
. Web. 8
Sept. 2011.
“Deductions.”
New Republic
4 Aug. 1920: 42
-
3.
EBSCOhost
. Web. 20 Mar. 2012.
Emerson, Caryl. "Leo Tolstoy On Peace And War."
PMLA: Publications Of The Modern
Language Association Of America
124.5 (2009): 1855
-
58.
Academic Search Premier
.
Web.
15 Mar. 2012.
Gilens, Martin. “Political Ignorance and Collective Policy Preferences.”
American Political
Science Review.
95.2 (2001): 379
-
96. Web. 29 Nov. 2011.
Goldfarb, Charles. “William Dean Howells: An American Reaction to Tolstoy.”
Comparative
Literature Studies
8.4 (1971): 317
-
37.
JSTOR.
Web. 12 Mar. 2012.
Griswold, Wendy. "The Fabrication of Meaning: Literary Interpretation in the
United States,
Great Britain, and the West Indies."
American Journal
of Sociology
92.5 (1987): 1077
-
115.
JSTOR.
Web. 13 Sept. 2011
.
Haslam, Paul Alexander. "The Evolution of the Foreign Direct Investment Regime in the
Americas."
Third World Quarterly
31.7 (2010): 1181
-
203.
Academic Search Premier
.
Web. 27 Nov. 2011.
Lee, Lillian, and Bo Pang. “Sentiment of Two Women: Sentiment Analysis and Social Media.”
1900 University Avenue, Cornell University, New York. 22 Mar. 2011. Lecture.
Li, V. “Misgivings of a Tongue
-
Tied Nation.”
Editorial Research Reports
2 (1990): n. pa
g. Web.
CQ Researcher.
13 Sept. 2011
.
Lippmann, Walter, and Charles Merz. “A Test of the News
: Introduction
.”
New Republic
4 Aug.
1920:
1
-
4
.
EBSCOhost
.
Web. 17 Mar. 2012.
Team POLITIC
18
Mallios, Peter Lancelot.
Our Conrad: Constituting American Modernity
. Stanford: Stanford UP,
2010.
Google Books.
Web. 15 Sept. 2011
.
Moser, Charles A. "The Achievement Of Constance Garnett."
American Scholar
57.3 (1988):
431.
Academic Search Premier
. Web. 20 Mar. 2012.
National Information Standards Organization.
Understanding Metadata.
Bethesda: NISO P,
2004. Web. 17 Mar. 2012.
Ohmann, Richard. "The Shaping Of A Canon: U.S. Fiction, 1960
-
1975."
Critical Inquiry
10.1
(1983): 199
-
223.
MLA International Bibliography
. Web. 13 Nov. 2011.
Pasterczyk, Catherine E. “R
ussian Transliteration Variations for Searchers.”
Education
Resources Information Center
8.1 (1985): n. pag. Web. 20 Mar. 2012.
Steyvers, Mark. "Probabilistic Topic Models."
Handbook of Latent Semantic Analysis
. Mahwah,
NJ: Lawrence Erlbaum Associates,
2007.
“TIFF Files.”
John Salim Photographic Glossary of Terms
. 2012. Web. 20 Mar. 2012.
Travis, Rick. "Problems, Politics, and Policy Streams: A Reconsideration US Foreign Aid
Behavior toward Africa."
International Studies Quarterly
54.3 (2010): 797
-
821.
Academic Search Premier
. Web. 27 Nov. 2011.
Wang, Xuerui, and Andrew McCallum. “Topics over Time: A Non
-
Markov Continuous
-
Time
Model of Topical Trends.” U of Massachusetts Dept. of Computer Science,
2006.
Web.
15 Mar. 2012.
Watson, Robert P., and Sean
McCluskie. "Human Rights Considerations and U.S. Foreign
Policy: The Latin American Experience."
Social Science Journal
34.2 (1997): 249
-
57.
Academic Search Premier
. Web. 27 Nov. 2011.
Team POLITIC
19
Appendices
Appendix A: Team Budget
Cost Per Item
Cost
Immediate Expenses:
MLA Guide Book
(already purchased from
MLA.org)
$22.00
Large External Hard Drive
(
1
+
Terabyte)
$300.00
Subtotal:
$322
.00
Foreseeable Expenses:
Hiring Technical Consultant for
Enhancement of Existing Tools
$1,500.00
Travel Expenses (Conferences)
$3,000.00
Subtotal:
$4,500.00
TOTAL:
$4,822.00
Team POLITIC
20
Appendix B: Team Timeline
Spring 2012
o
Complete team website
o
Continue literature r
eview
o
Begin
scanning periodicals into constructed Russian literature database
o
Begin annotating Russian literature database and select metadata to capture
o
Begin coordination with MITH and start to familiarize team with methods of
constructing and analyzing databases
Attempt to automate metadata collection
Summer 2012
o
C
ontinue scann
ing and annotation of Russian literature database
Fall 2012
o
Prepare for
and present at
Junior Colloquium
o
Determine methods by which to quantify American foreign policies
Begin construction of Foreign attitude / policy database
Spring 2013
o
Present at
Undergraduate Research Day
o
Being drafting team t
hesis
Summer 2013
o
Continue to draft team t
hesis
Fall 2013
o
Obtain feedback for our thesis paper from Dr. Mallios
o
Gather data regarding
American foreign p
olicy
toward Russia
o
Draw conclusions regarding the
relationship between American foreign policies
and r
eception of
Russian
literature
Winter 2013
-
14
o
Prepare p
resentation for Thesis Conference
o
Revise and edit team t
hesis
Spring 2014
o
Presen
t at Senior Thesis Conference
Team POLITIC
21
Appendix C: Current Annotation
Guidelines
1.
Author (or authors) of principal concern in article
. What literary author or authors, if
any, is this article primarily about?
•
Spelling
:
--
Be sure to spell any names given in answer to this question
as accurately as possible
,
exactly
reproducing how the name is spelled in this article. (Spellings will differ between
articles: we want to capture the differences.
--
Include the
fullest version
of the author’s name included in the article: i.e., include an
author’s first and/or middle nam
es and/or initials if these names are included at any point
in the article.
•
Individuals
:
Only literary authors named by
personal name
(i.e., not anonymous figures
or those referenced only by job title) and who are
persons
(i.e., not publications) count
as
“authors” for purposes of this question.
• “Literary author” means an author of fiction, poetry, plays, or related forms of creative
writing. This applies whether the author is being invoked in his or her capacity as a
literary writer or not.
Academic
professors, literary critics, and journalistic and other
commentators on literature do not fall into this category, unless they have significant
literary accomplishments of their own.
• An author is of “principal” or “primary” concern in an article when a
n author is a major,
continual, or focal concern that runs and receives explicit mention
throughout
an article
as part of its general field of concerns, not just in discrete or severable paragraphs of it.
• Some more rules of thumb on identifying whether
an author is a “primary” or
“principal” concern in an article:
• if a literary author’s name is
included in the article’s title
, it is likely that s/he should
be included in the answer to this question
• if there is a large disproportion between the numb
er of times different authors are
mentioned or referred to, this is a good indicator that those mentioned less should likely
not
be included in the answer to this question
Team POLITIC
22
• if the excising of relatively few paragraphs from this article would result in th
e
elimination of reference to an author, that author should generally
not
be included in the
answer to this question
• as a general matter,
construe answers to this question narrowly
: only an author (or
authors) comprising the main and consistent focus of
an article should be included
—
although articles whose explicit focus is evenly to compare two (or more) authors
throughout may be described as having multiple “principal” authors
2.
Sentiment Analysis 1: the Opinion of the Article Writer
. Which of the following
ratings comes closest to the
article writer’s
expressed opinion of the literary author(s) this article
principally concerns? [Note: this question concerns the opinion ultimately taken by the
article
writer him/herself
on the litera
ry authors question. This is so even though the article writer may
quote or reference opposing opinions along the way.]
This question should be answered
separately for each author named in question1.
2
–
A Positive Opinion
: a generally or ultimately posit
ive opinion as an overall matter.
0
–
A Negative Opinion
: a generally or ultimately negative opinion as an overall matter.
U
–
A Mixed or Unclear Opinion, or No Opinion Offered
: it is not possible to say
whether the writer’s overall opinion of an author is
either positive or negative because the
writer’s opinions are mixed, unclear, or not offered at all.
3.
Sentiment Analysis 2: Uncertainty of Article Writer’s Opinion.
If the answer to
Question 2 is “U,” answer the following question; if not skip it. Which o
f the following ratings
comes closest to describing why the article writer’s opinion of a principal literary author is
unclear?
This question should be answered separately for each author named in question 1.
1
–
A Mixed or Unclear Opinion
: the article wr
iter either expresses mixed opinions
about the literary author, or does not make clear how the opinions, judgments, or values
s/he holds clearly relates to the literary author
X
–
Straight Factual Account
: this is not an article in which the article writer
’s
personality, opinions, judgments, are in evidence; the article writer assumes the position
of the “straight,” factual, objective newspaper reporter; the article writer’s stance is
neutral
with respect to his/her own opinions and values, not evaluative.
4.
Sentiment Analysis 3: Principal Author as Subject of Debate.
(Y/N) Does this article
contain any explicit reference to the literary author(s) it principally concerns as a subject of
debate, either because interpretations of that literary author’s meaning
are explicitly disputed, or
because opposing positive and negative opinions of an author are explicitly referenced?
Team POLITIC
23
5.
Books mentioned?
(Y/N). Does this article explicitly mention by title any specific
books, poems, or texts written by any literary author it is principally about?
Note
: this question
should be answered separately for each author named in question 1.
6.
National identificatio
n
. (Y/N) Does this article specifically identify the nationality of
any literary author it is principally about?
Note
: this question should be answered separately for
each author named in question 1.
7.
Style or literary artistry as issue
. (Y/N) With respect
to any literary author this article is
principally about, is the author explicitly described in terms of “art” or as an “artist” or in terms
of his or her “artistic” vision, or is at least one paragraph of the article devoted to the style (not
the content
) of his or her writing? (A “yes” answer to any part of this question means a YES
answer to the question as a whole.)
Note: this question should be answered separately for each
author named in question1.
8.
Foreign Place Names
. (Y/N) Are there any non
-
U.S. p
lace names mentioned in this
article?
9.
Gender of Article Writer
. Use the following scale to identify the apparent gender of the
writer of this article (i.e.,
no
t
the gender of the literary figure(s) in question, but the gender of the
article writer who is writing about the literary figure(s)):
M
–
Male
F
–
Female
U
–
Unclear (i.e., because name is ambiguous or initials are used; the article is unsigned;
or for an
other reason)
10.
Gender as Issue
. (Y/N) Is gender ever explicitly discussed as an issue in this article?
• Note: The fact that a character or author discussed in the article is a man or woman is
not sufficient to constitute a Yes answer to this question; th
ere needs to be some explicit
attention drawn to gender as a matter of significance
—
(if only in a single phrase)
--
or
reflection on or significance attributed to the categories of “man” or “woman,”
“masculine” or “feminine,” or other gender ideas.
11.
Race as
Issue
. (Y/N) Is race ever explicitly raised as an issue in this article?
• Note: this question should be answered “Yes” only if: (i) the article explicitly uses the
term “race” (or some direct variant on it: “racial,” “racism,” etc.); (ii) there is expl
icit
discussion about general ideas of race; or (iii) one of the following
radicalized
categories
is explicitly invoked: black or African; white or Aryan or Caucasian; Slavic; Jewish or
Hebrew.
Team POLITIC
24
12.
Socioeconomic class as issue
. (Y/N) Does socioeconomic class
receive explicit
discussion in this article?
• Note: Any explicit mention of social class (for example, “aristocratic,” “peasant,” “the
poor,” “Count,” “prince”) will qualify as a YES answer to this question. (Czar, however,
as a state figure, does not al
one qualify.)
13.
Religion as Issue
. (Y/N)
Does religion receive explicit discussion in this article?
14.
Radical Politics as issue
. (Y/N) Do any radical political movements including
anarchism, nihilism, bolshevism, socialism, or communism receive explicit
mention in this
article?
15.
America/West invoked as a point of similarity with Russia
. (Y/N) Does this article
make any specific and explicit claims that Russia shares any quality in common with the U.S.,
“the West,” or any of the countries, cultures, and/or
literatures of Western Europe?
16.
America/West invoked as point of contrast with Russia.
(Y/N) Does this article draw
any specific and explicit contrasts between Russia or anything Russian and any qualities or
aspects of the U.S., “the West,” or any of the
countries, cultures, and/or literatures Western
Europe?
Team POLITIC
25
Appendix D
: Sample Annotation
Question
Evolution
Current Sample Annotation Question
4.
Sentiment Analysis: Principal Author as Subject of Debate.
(Y/N) Does this article contain
any explicit reference to the literary author(s) it principally concerns as a subject of debate, either
because interpretations of that literary author’s meaning are explicitly disputed, or because
opposing positive and neg
ative opinions of an author are explicitly referenced?
Original Sample Annotation Question
4.
Sentiment Analysis: All Opinions Expressed in the Article
. [This question concerns
all
opinions expressed in the article concerning the literary writers in ques
tion
—
whether they
express the article’s own point of view or other perspectives quoted and referenced in the
article.] Which of the following ratings comes closest to the
entire field
of opinions quoted or
mentioned in this article concerning each of the l
iterary authors the article principally concerns?
Note: this question should be answered separately for each author named in question 1.
2
–
A Positive Opinion
: a generally or ultimately positive opinion as an overall matter
1
–
A Mixed or Unclear Opinion
:
such that it is not possible to say whether the article’s
overall opinion of an author is positive or negative
0
–
A Negative Opinion
: a generally or ultimately negative opinion as an overall matter
X
–
Neutral
: This article is not evaluative: it does not
express opinions about the
author(s) in question, but is rather strictly and neutrally factual
Team POLITIC
26
Appendix E
: Search Results Using the Readers’ Guide Retrospective
Author / Subject
Total # Search Results
Tolstoy, L.N.
432
Chekhov, A.P.
266
“russian
literature”
ㄹN
䑯獴潥癳vyⰠc⹍.
ㄲN
䝯dkyⰠ䴮
ㄲN
呵q来湥瘬vf⹓.
㤶
B牥獨歯
J
B牥獨潶獫syaⰠ䔮b.
㔳
䅰灥湤楸⁆
: Alternative spellings of “Dostoevsky”
“dostoevsky” OR “dostoyevsky” OR “dostoevskii” OR “dostoyevskii” OR “dostojevsky” OR
“dostojevskii” OR “dostoeffsky” OR “dostoyeffsky” OR “dostoeffskii” OR “dostoyeffskii” OR
“dostoieffsky” OR “dostoievsky” OR “dostoieffskii” OR “dostoievskii” OR “dosteovsky” OR
“dostoyefsky” OR “dostoievski” OR “dosteoffsky” OR “dosteovskii” OR “dostoefsk
y” OR
“dostoefskii” OR “dostojefsky” OR “dostojefskii” OR “dostojefski” OR “dostoevski” OR
“dosteovski” OR “dostoyevski” OR “dostojevski” OR “dostojeffski” OR “dostoyeffski” OR
“dostoeffski” OR “dostoieffski” OR “dostoievski” OR “dostojefski” OR “dostoyefs
ki” OR
“dostoefski” OR “dostoiefski”
Alternative spellings research conducted by Nick Slaughter of the Foreign Literatures in
America project.
Team POLITIC
27
Appendix
G
: Sample Chart of Periodicals within Readers
’ Guide Retrospective: 1890
-
1982
Source
Type
ISSN /
ISBN
Publication Name
Publisher
Indexing
Start
Indexing
Stop
Magazine
0163
-
2027
50 Plus
Reader's Digest Association, Inc.
1/1/83
11/1/88
Magazine
1548
-
2014
AARP the Magazine.
AARP
5/1/03
Magazine
1041
-
102X
Ad Astra
National Space Society
1/1/89
Magazine
0955
-
2308
Adults Learning
National Institute of Adult
Continuing Education
1/1/95
Magazine
0001
-
8996
Advocate
Regent Media
1/16/01
Magazine
0002
-
0966
Aging
Superintendent of Documents
11/1/82
1/1/96
Academic
Journal
1205
-
7398
Alternatives
Journal
University of Waterloo
1/1/05
Magazine
Amazing Wellness
Active Interest Media, Inc.
1/1/10
Magazine
1545
-
8741
Amber Waves: The
Economics of Food,
Farming, Natural
Resources, & Rural
America
U.S. Dept. of Agriculture
Economic Research Service
2/2/04
Magazine
0002
-
7049
America
America Press
1/1/83
Magazine
0002
-
7375
American Artist
Interweave Press, LLC
1/1/83
Magazine
1540
-
966X
American Conservative
American Conservative
1/16/06
Magazine
1079
-
3690
American Cowboy
Active Interest Media,
Inc.
2/1/11
Magazine
0194
-
8008
American Craft
American Craft Council
2/1/83
Academic
Journal
0002
-
8304
American Education
US Department of Education
12/1/82
1/3/85
Magazine
0361
-
4751
American Film
BPI Communications
1/1/88
1/1/92
Magazine
0002
-
8541
American Forests
American Forests
9/1/92
Academic
Journal
1549
-
4934
American Geographical
Society's Focus on
Geography
Wiley
-
Blackwell
10/15/05
Magazine
1523
-
3359
American Health
RD Publications Inc.
1/1/99
10/1/99
Magazine
0730
-
7004
American Health
(0730
-
7004)
RD Publications Inc.
1/1/88
1/1/97
Magazine
1092
-
1656
American Health for
Women
RD Publications Inc.
12/1/96
1/1/99
Magazine
0002
-
8738
American Heritage
AHMC Inc.
2/1/83
Magazine
1076
-
8866
American History
Weider History Group
6/1/94
Magazine
0002
-
8770
American History
Illustrated
Weider History Group
1/1/83
3/1/94
Academic
Journal
0095
-
182X
American Indian
Quarterly
University of Nebraska Press
1/1/90
Academic
Journal
1067
-
8654
American Journalism
Review
University of Maryland
3/1/93
Academic
Journal
0003
-
0937
American Scholar
Phi Beta Kappa Society
1/15/83
Team POLITIC
28
Appendix H: Glossary of Terms
Classification
: Supervised (requires human input) method of analyzing text in which the user
first defines labels of how they want a collection of words, sentences, etc, to be classified.
Next, the user creates a training corpus of words, sentences, etc that is already classified
according the specified labels to train the software. The user ca
n then input the collection
of words, sentences, etc
.
they want to “classify” by the labels.
Corpus:
a large body of texts, often the entirety of works by an author, articles by a newspaper,
or writings about a certain subject
Keyword frequencies
:
How
often a word appears in literature
Latent Dirichlet Allocation
: Abbreviated LDA, attributes each word in a written document to a
select number of topics determined to compose the document
Optical Character Recognition Software
: Abbreviated OCR, translat
es PDFs and scans of either
handwritten or typed texts into electronic machine readable text
Semantic Parsing/Analysis
: Also known as opinion mining, using text analysis to determine
subjective information in written works
Shalmaneser
: A supervised
tool (
requires human input) for semantic and syntactic parsing, which
automatically assigns text to semantic and syntactic classes. Generates output such as the
following figure:
Team POLITIC
29
Where the original sentence is:
“
Creeping in its shadow I reached a
point whence I could
look straight through the uncurtained window.
”
The green text is the generated analysis of the seman
tics of the sentence and the gra
y text
is the generated analysis of the syntax of the sentence.
Text Analysis Portal for Research
:
Abbreviated TAPoR, a collaborative project that permits
researchers to use text analysis tools for the Humanities
Topic modeling
: The use of a
type of statistical model that
generates abstract “topics” in a
database of documents
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