










































  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

 
 

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TOPIC MODELING AND 
ANALYSIS: 

 COMPARING THE MOST 
COMMON TOPICS IN  

19TH-CENTURY NOVELS 
WRITTEN BY  

FEMALE WRITERS  

LAUREN RHA, 
SEAN SILVER (FACULTY ADVISOR) 

 

✵ ABSTRACT 
Women authors from the 19th-century have 

had a profound impact on the literary world due to 
their critical approach to and inclusion of various so-
cial phenomena within their work, such as women's 
rights, sexuality, and human psychology. This paper 
seeks to contribute to the discussion by quantifying 
thematic similarities in eight select novels by various 
female authors of the 19th-century. These novels 
were chosen due to their contribution to literature 
and their popularity, common use in college courses 
around the world, and the prominence of the female 
authors. This study included utilizing a programming 
environment known as R Studio to perform a topic 
model. Performing a topic model allowed for the 
discernment of ten main themes or topics that can 
be generally seen across all eight selected novels, 
and by extension, 19th-century literature by female 
authors. The research found initial evidence to sup-
port the general understanding of said literature as 
an endeavor of the themes of social critique and in-
dividual consciousness; however, the results were 
not absolute in conclusion because of the limited 
size of the corpus. A larger corpus of documents 
(novels) is necessary to reach further conclusions.   

1 INTRODUCTION 
In the 19th-century, iconic texts by female au-

thors revolutionized prose fiction and imaginary 
work written in narrative form through their criticism 
of social expectations for women, commentary on 
the class system and expressions of sexuality, and 
unique focus on characters’ psychological states and 
moral developments. This paper seeks to contribute 
to the discussion by quantifying and analyzing these 
thematic similarities in eight select novels by the fol-
lowing female authors of the 19th-century: Jane Aus-
ten, Charlotte Brontë, Emily Brontë, Anne Brontë, 
George Eliot, Louisa May Alcott, and Elizabeth Gas-
kell. Performing such a topic model on literary stud-
ies is not yet widely done, although it has proven to 
successfully extract themes and topics from word-
frequency data. In doing so, perhaps computation-
ally generated themes can be formed and present a 
new perspective on common and well-studied 
themes in the 19th-century literature of various fe-
male authors.  

This research uses an approach known as 
“distant reading” for its analysis. “Distant reading” 
demonstrates the value of reading large numbers of 
text together and relies on computer-assisted mod-
eling to analyze a larger amount of texts than can be 
read by any one individual.[15] “Distant reading” and 
the specific practice of topic modeling was per-
formed within the R Studio programming environ-
ment. The R Studio programming environment is a 
development environment for R, a programming 
language used for statistical computing and gra-
phics. The novels analyzed by the author included 
Jane Austen’s Emma, Charlotte Brontë’s Jane Eyre, 
Emily Brontë’s Wuthering Heights, Anne Brontë’s 
The Tenant of Wildfell Hall, George Eliot’s Middle-
march, Louisa May Alcott’s Little Women, Elizabeth 
Gaskell’s North and South, and George Eliot’s The 
Mill on the Floss. These eight novels were chosen for 
their popularity, common usage within college clas-
ses, contribution to the discipline of literature, and 
the prominence of their female authors. This re-
search explains how the novels were prepared for 
topic modeling and produces a specific topic model 
visualization, an “interactive heatmap,” to compare  
the frequency of topics found in each novel. 
 



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

2 METHODOLOGY  

INSTALLATION OF PACKAGES & PREPARING THE CORPUS INTO A 

DOCUMENT TERM MATRIX 

The first step, distant reading, involved the prepara-
tion of a corpus of documents into a document term 
matrix. A document term matrix represents the 
words inside a text as a table (or a matrix) of num-
bers. The document term matrix format was used to 
implement topic modeling. In doing so, the novels 
could be read as data instead of narratives.  

The text file used included the following 
novels in one document: Jane Austen’s Emma, Char-
lotte Brontë’s Jane Eyre, Emily Brontë’s Wuthering 
Heights, Anne Brontë’s The Tenant of Wildfell Hall, 
George Eliot’s Middlemarch, Louisa May Alcott’s Lit-
tle Women, Elizabeth Gaskell’s North and South, and 
George Eliot’s The Mill on the Floss. All of these texts 
had similar base content material; they all had a fe-
male character as their main protagonist. They are all 
also well-studied novels throughout the world, as 
many consider these texts “classics.” These texts 
were accessed through the digital archive Project 
Gutenberg. 

First, the document was reviewed and all the 
extraneous metadata was removed. The extraneous 
metadata included introduction pages, acknowl-
edgments, publishing details, title pages, and the ta-
ble of contents. The only text within the document 
was from the actual story of each novel. Next, each 
chapter in the document was labeled in a single con-
tinuous document. For example, Emma, which has 
55 chapters in total, was numbered from 1-55. The 
following novel was Jane Eyre, which started at 56 to 
continue the document. This was done in order to 
make the coding process and analysis by R Studio 
easier. By combining all of the novels into one text 
document, there would only need to be one file 
scanned and coded into the program rather than 
eight individual ones.  

Each chapter was arranged into a contin-
gency table that organized the most frequent words 
found to the least. Then, a relative frequency table 
was generated by dividing the total count of every 
word (token) in the chapter text by the total word 
count in a chapter. A function, stopword(), which was 
coded by a professor in the English Department at 

Rutgers University – New Brunswick, Professor Sean 
Silver, was used to remove words such as “the,” “a,” 
and “and,” as well as character names. This was done 
because otherwise, the most frequently appearing 
words would be these words, which do not repre-
sent any theme that can be applied across all eight 
texts. The text was then transformed into a docu-
ment term matrix, ready to be prepared and used 
through topic modeling.  

 
TOPIC MODELING: A DESCRIPTION 

Topic modeling refers to a coding technique that is 
able to process and analyze large-scale amounts of 
data (corpus) into key themes or topics. When ana-
lyzing novels through a technological lens, there are 
often two problems: the ambiguity of definition of 
certain words (i.e. a word like “fan” holds two mean-
ings: a cooling apparatus and an enthusiast for a par-
ticular subject), as well as processing large data sets. 
The former is understood situationally; the human 
mind is able to identify, through context and critical 
thinking, the proper definition of such words. How-
ever, the computer often struggles with – to call on 
the aforementioned example – separating the two 
types of “fans.” In this case, a method known as “key 
word in context” is used. This method allows the pro-
grammer to see the word within the context of the 
text, so as to better discern what the proper defini-
tion for the word is. Conversely, unlike the computer, 
it is very difficult for humans to read and interpret in-
credibly large amounts of data.[3] As a result, it was 
imperative that the technique known as topic mod-
eling was used to address and offer solutions for 
both of these concerns.  

Within topic modeling, the identified large-
scale document is “read” by the computer and sep-
arated into key themes or topics. These topics are 
determined by the likelihood that certain words will 
appear together within the novel; each topic consists 
of words that have similar meanings or often emerge 
in context of each other. This is known as “colloca-
tion” and is essentially equivalent to concepts or 
themes. After the identification of major themes, the 
estimated proportions of the topics within the docu-
ments are identified. In this research, a visualization 
was then generated: an interactive heatmap. The in- 



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

  
 
 
 
teractive heatmap is a graphical representation of 
the data where each of the individual values that are 
contained within the matrix is represented as differ-
ent colored squares. When hovering over each col-
ored square, the row label, column label, and calcu-
lated value of the data appears. In this way, topic 
modeling hovering allows for the organization and 
summarization of “electronic archives at a scale that 
would be impossible by human annotation.”[3]  
 The number of topics/themes one can gen-
erate (“k”) is arbitrary and up to the discretion of the 
programmer. If the “k” value chosen was too small, 
the topics generated would be too broad to classify 
as “main themes.” However, if the “k” value chosen 
was too large, there would be too many overlapping 
topics.  

FIGURE A: INTERACTIVE HEATMAP OF TOPICS MOST FREQUENTLY FOUND IN 19TH-CENTURY NOVELS BY FEMALE AUTHORS 

Generated interactive heatmap of topics most frequently found in 19th-century novels. 

 

FIGURE B: Examples of the interactive square. Each inter-
active square displayed the name of the novel, the 
topic, and a value that represented the sum of all of the 
probability values generated in the topic modeling se-
quence.  



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

  

 
 
FIGURE C: The 10 generated topics in TRIAL 5. 

 

 
FIGURE D: The 10 generated topics in TRIAL 6 for comparison. 



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

 TOPIC MODELING: IMPLEMENTATION 

Latent Dirichlet allocation (LDA), the simplest topic 
model, was employed within this research, as the “in-
tuition behind LDA is that documents exhibit multi-
ple topics [themes].”[3]  In LDA, “all the documents [in 
this case, the novels] in the collection share the same 
set of topics, but each document exhibits those top-
ics with different proportion.”[11] I chose to have LDA 
generate ten topics/themes (𝑘𝑘 = 10) and to return 
the top 25 terms from each topic and the estimated 
proportions of topics in the documents. Because the 
corpus of documents was divided into chapters and 
not novels, I had to create a separate code to differ-
entiate certain chunks of chapters as certain novels. 
For example, I coded for chapters 1-55 to be for 
Emma, chapters 56-93 to be Jane Eyre, and so forth. 
Using this data, I then plotted the generated values 
that show the estimated proportions of topics in 
each novel.  

There were ten total trials of the interactive 
heatmap and topic modeling performed.  
 

3 RESULTS 
After performing ten trials of the interactive 

heat map and topic modeling, although there was a 
slight variation in the order of words and changes in 
a few number of terms each trial, results displayed 
that the generated topics and terms were generally 
the same. TRIAL 5 was randomly chosen. Any trial 
would have sufficed because there was stability be-
tween the various trials and the generated themes.    
In general, there were not many topics that were 
found prominently amongst all of the novels – only 
TOPIC 6 was found averagely within all of the novels. 
Instead, there were concentrations of certain topics 
in certain novels. This can be seen in TOPIC 3’s preva-
lence in Emma and TOPIC 1 and TOPIC 4’s prevalence in 
Middlemarch, and so forth. Experimentation on this 
topic has led to the conclusion that the most popular 
word in a topic changes about forty percent of the 
time between essentially identical topic models.  

The most significant and comprehensive 
themes amongst the novels included TOPIC 4 and  
TOPIC 6. TOPIC 4 touched upon much of women’s worth 
within the social constructions of the 19th-century 
(“husband,” “life, “marriage,” “self”), while TOPIC 6 was 

focused more on the mind and body (“eyes,” “face,” 
“look,” “heart,” “hand,” “felt,” "voice”). The rest of the 
topics – although they were found in some capacity 
amongst all the novels, were generally only concen-
trated within one or two novels and did not show a 
real, significant, or comprehensive theme. Instead, it 
can be understood that the remaining themes 
mainly consisted of a general melting pot of the 
most commonly appearing words within each re-
spective novel (perhaps due to the plot, characteri-
zation of certain characters, and/or an individual writ-
ers’ writing style as opposed to chosen themes).  

 

4 DISCUSSION 
Although it is difficult to come to concrete 

conclusions about shared themes/topics because 
the corpus was small in comparison to analyzing 
thousands of novels written by female authors in this 
century, I still found some support through the gen-
eration of topics based on word frequency for our 
understanding of 19th-century literature by female 
authors as an endeavor of social critique and individ-
ual consciousness of women during the 19th-century.  

Using the technique of topic modeling (in-
teractive heatmap), I assessed eight texts by seven 
unique Anglophone female writers. Multiple trials 
were performed in order to distinguish and observe 
which themes appeared in highest frequency, as 
well as to assess whether there was a notable differ-
ence in the generated themes for each respective 
trial. Although there was slight variation in the order 
of words on the spreadsheet and a few number of 
terms changed each trial, the results repeatedly 
showed the keywords to be virtually the same  
(FIGURE C compared to FIGURE D). For example, if one 
trial had the word “family” within a topic about 
agreeableness and neighborly interaction (TOPIC 1 in 
FIGURE C), another trial may not have the specific word 
of “family,” but still had keywords such as “quite,” “fa-
ther,” “woman,” and “little.” This is due to the nature 
of topic modeling. Within topic modeling, a label is 
automatically assigned to each topic based on its 
top term. Then, the code changes the topic model 
in various ways to see if the decided label turns up 
again.[16]  

19th-century literature is often characterized  



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

by its understanding of the psychological state 
within stressful and restrictive social environments or 
when confronted by moral dilemmas.[13] 19th- century 
literature dealt with ideas of moral decline and de-
velopment and addressed the different modes and 
extent of control within certain psychological, social, 
cultural, and moral frameworks. It “delve[d] [into] the 
internal life of the characters… through a focus on 
the thoughts and motivations of the characters ra-
ther than their occupations and external settings 
alone.” [13] 

 TOPIC 6 is indicative of this; it included words 
such as “felt,” “mind,” “love,” “eyes,” “face,” “stood,” 
and “looked.” As seen from the interactive heatmap, 
this topic is found averagely in all of the texts.  
TOPIC 6’s words have an emphasis on mind and body. 
Words such as “felt,” “mind,” and “love” represent 
the psychological state and emotion, while words 
such as “eyes,” “face,” “stood,” and “looked” are 
grounded to the physical body; this connection be-
tween mind and body can be interpreted as repre-
sentations of self-control. Within Sally Shuttleworth’s 
study, Charlotte Bronte and Victorian Psychology, 
she meditates on this idea of self-control, writing: 
“rigorous control and regulation of the machinery of 
mind and body would offer a passport to autono-
mous selfhood and economic liberty.”[14] However, 
she also importantly notes that there are “two con-
flicting models of psychology found in Victorian eco-
nomic discourse: the individual is figured both as an 
autonomous unit, gifted with powers of self-control, 
and also as a powerless material organism, caught 
within the operations of a wider field of force.”[14] In 
considering this, TOPIC 6, – rich with words such as 
“felt” and “look,” – strengthens the general under-
standing and characterization of 19th-century litera-
ture and its preoccupation with the consciousness 
and social standing in society.  

Within these novels, “males hold positions of 
political, institutional, and sometimes of economic 
power denied to females,” which can be seen in  
TOPIC 4, with words such as “husband,” “feeling,” 
“self,” “marriage,” “world,” “living,” and “experi-
ence.”[14] This topic touches on the aspect of 19th-
century society that defined much of a woman’s legal 
and social worth. Although this connection is in line 

with our understanding of Victorian society, it was 
significant that TOPIC 4 on women’s worth based on 
social construct was not as prominent as TOPIC 6 on 
the mind and body in all of the texts. This showed 
the authors’ intent to put an emphasis on the psy-
chology and inner world of these female characters, 
rather than their marriage, in defining them and giv-
ing them a voice as individuals. The prominence of 
TOPIC 6 demonstrated that “females hold a kind of 
psychological and moral power that is exemplified in 
their status as paradigmatic protagonists.”[12]  

In addition, within a quantitative study con-
ducted by John A. Johnson, Joseph Carroll, Jona-
than Gottschall, and Daniel Krueger, the data con-
cluded that within 19th-century Victorian literature, 
female protagonists scored the “highest of Agreea-
bleness,” among other characteristics. Agreeable-
ness was defined as a “pleasant, friendly disposition 
and tendency to cooperate and compromise, versus 
a tendency to be self-centered and inconsider-
ate”.[12] This tendency toward altruism is “more 
strongly associated with maturity, [and]… as vehicles 
for improving society.”[12] Although this could be un-
derstood as a great emphasis on the improvement 
of society as a whole, it also demonstrates the female 
characters’ need to be overwhelmingly “pleasant” 
within society. It was unsurprising that TOPIC 3 of my 
research included words that evoke some sort of 
community or degree of agreeableness amongst 
neighbors, such as “quite,” “little,” “time,” “sure,” and 
“dear.” As I was interested in understanding the 
proper context of these words, I performed a key-
word in context. Words like “quite” were used to ex-
press full and absolute agreeableness on a subject; 
examples include: “to feel quite sure,” “something 
quite fresh,” and “you do quite right.” Similarly, the 
keyword “sure” evoked ideas of obliging manner-
isms; examples include: “To be sure,” “I am sure of 
having their opinions with me,” and “I am sure you 
are a great deal too kind.”   

The heavy concentration of this “agreeable- 
ness” theme within the first novel, Emma, is quite ap-
propriate of the text, as it is a novel about a young 
female and her social life, experiences, and relation-
ships within the small town of Highbury. Although 
the novel is an exploration of Emma’s relationship 



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

with her mind and body (ideas of maturity and de-
velopment), she comes to develop her understand-
ing of self-control and her emotional state through 
her interactions with others.  

As mentioned before, I still found certain 
words and, thus, topics that were, understandably, 
more concentrated within certain novels. As just dis-
cussed, TOPIC 3 was very concentrated within Emma. 
Although this was the most prominent case, in gen-
eral, there seemed to be topics that were strongly 
associated with one particular novel over the others.  

 

5 CONCLUSION 
This research aimed to add to the current 

discussion surrounding 19th-century literature by fe-
male authors by quantifying and analyzing various 
thematic elements within eight select novels. These 
themes included social expectations for women, 
commentary on class and expressions of sexuality, 
and a focus on the protagonist’s psychological state 
or moral development. This research is significant 
because it allowed a new method of analysis of 19th-
century literature by female authors. The method of 
topic modeling utilized within the R Studio coding 
environment is effective in generating themes based 
on word-frequency data, but it must be noted that 
there are limitations to this method of analysis as it 
completely rejects the traditional format of reading 
novels. Within novels, there is a plot and specific 
storyline. The novel is organized and divided in a 
way that best supports the development of this 
plotline. However, topic modeling does not discern 
nor care for the sequence of the story and is instead 
only interested in quantifying the text – or “data” – 
and extracting and putting certain groups of words 
into “themes” or “topics” so as to quantitatively dis-
play the author and novel’s thematic intent.   

This research would have benefited from a  
larger corpus of works in order to come to more con-
clusive, specific, and generally applicable themes to 
be applied to novels during this time period. In ad- 
dition, although words such as “you,” “I,” and “it” 
were included within the stopword() function, they 
still appeared in the results. They were joined by a 
strange symbol: <e2>, which I was regrettably una-
ble to remove. This was most likely due to oversight 

within the coding script. Ideally, the analyses would 
be redone with a fully cleaned corpus because the 
appearance of the <e2> symbol limits the ability to 
draw conclusions and undermines the interpretabil-
ity of the topic wordlists.  

Although the results of this analysis on the 
famous novels of 19th-century female writers were 
telling, they were not absolute in their conclusion 
and lacked a large enough corpus to properly deter-
mine the main themes amongst a large number of 
Victorian novels. If repeated, this research should 
take on a larger number of novels. There was some 
evidence of the societal restrictions imposed on 
women during this time period (TOPIC 4), yet con-
versely, there was also some evidence of the authors’ 
intent to dispel these societal, political, and legal fac-
tors in defining the female individual by defining 
them through their mind, consciousness, and body 
(TOPIC 6). Evidence showed the importance of psy-
chology, the body, and the inner worlds of their fe-
male protagonists in relaying the stories of women 
and giving them the opportunity to voice their 
thoughts and feelings through literature∎  

 
6 ACKNOWLEDGEMENTS 

Thank you to Professor Sean Silver of the 
English department at Rutgers University—New 
Brunswick for his assistance and encouragement of 
this research.  

 
  



  ARESTY  RUTGERS UNDERGRADUATE RESEARCH JOURNAL, VOLUME I, ISSUE III 
 
 
 

7 REFERENCES  
[1] Alcott, Louisa May, Little Women. (Melbourne; London; Bal-

timore: Penguin Books, 1953) 
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[2]  Austen, Jane, Emma. (London; New York: Penguin Books, 
2003) 

 HTTPS://WWW.GUTENBERG.ORG/EBOOKS/158 
[3]  Blei, David. “Introduction to Probabilistic Topic Models.” 

(2011) 
 HTTPS://WWW.EECIS.UDEL.EDU/~SHATKAY/COURSE/PAPERS/UINTROTO-

TOPICMODELSBLEI2011-5.PDF 

[4]  Brontë, Anne, The Tenant of Wildfell Hall. (Oxford: Blackwell, 
1931) 

 HTTPS://WWW.GUTENBERG.ORG/EBOOKS/969 
[5]  Brontë, Charlotte, Jane Eyre. (New York: Penguin Books, 

1985) 
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[6]  Brontë, Emily, Wuthering Heights. (London; New York: Pen-
guin Books, 2003) 

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[7]  Burrma, Rachel. “The Fictionality of Topic Modeling: Ma-

chine Reading Anthony Trollope's Barsetshire Series.” Big 
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[8]  Eliot, George, Middlemarch. (London: Vermont: J. M. Dent; 
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[9] Eliot, George, The Mill on the Floss. (Edinburgh; London: W. 
Blackwood and Sons, 1860) 

 HTTPS://WWW.GUTENBERG.ORG/EBOOKS/6688 

[10] Gaskell, Elizabeth Cleghorn, North and South. (Harmonds-
worth, Penguin, 1970) 

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[11] Jockers, M. L., and Mimno, D. “Significant themes in 19th-
century literature.” Poetics 41 (2013): 750–769.  

[12] Johnson, J. A., et al. “Portrayal of personality in Victorian nov-
els reflects modern research findings but amplifies the sig-
nificance of agreeableness.” Journal of Research in Personal-
ity (2010) 
HTTP://PERSONAL.PSU.EDU/FACULTY/J/5/J5J/PAPERS/VICTORIANPERSONALITY.PDF  

[13] Sen, Debashish. Psychological Realism in 19th Century Fic-
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[14] Shuttleworth, Sally. Charlotte Brontë and Victorian Psychol-
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[15] Underwood, Ted. "A Genealogy of Distant Reading.” Digital 
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[16] Yang, Y., et al. “The Stability and Usability of Statistical Topic 
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tems, 6, no.2, article 14 (2016) 
HTTPS://DL.ACM.ORG/DOI/ABS/10.1145/2954002?DOWNLOAD=TRUE 

 

 
 
 
 
Lauren Rha is a rising first-year graduate student at the Rutgers Graduate School of Education. She 
hopes to become an ESL teacher for immigrants and refugees coming into the United States. Her 
interest in and study of 19th-century literature motivated her to research common themes across 
eight famous Victorian novels. The research presented was done as a final project within Professor 
Sean Silver's English 315 class during her second semester as a junior, where she learned the fun-
damentals of basic coding for literary analysis within the R Studio environment. 

 

 

https://www.gutenberg.org/ebooks/514
https://www.gutenberg.org/ebooks/158
https://www.eecis.udel.edu/%7Eshatkay/Course/papers/UIntrotoTopicModelsBlei2011-5.pdf
https://www.eecis.udel.edu/%7Eshatkay/Course/papers/UIntrotoTopicModelsBlei2011-5.pdf
https://www.gutenberg.org/ebooks/969
https://www.gutenberg.org/ebooks/1260
https://www.gutenberg.org/ebooks/768
https://works.swarthmore.edu/fac-english-lit/286
https://www.gutenberg.org/ebooks/145
https://www.gutenberg.org/ebooks/6688
https://www.gutenberg.org/ebooks/4276
http://personal.psu.edu/faculty/j/5/j5j/papers/VictorianPersonality.pdf
http://www.digitalhumanities.org/dhq/vol/11/2/000317/000317.html
https://dl.acm.org/doi/abs/10.1145/2954002?download=true

