big modernism

Topic Modeling for Literary Analysis BIG MODERNISM

Belaid Moa, PhD Computing Specialist, Compute Canada Jana Millar Usiskin PhD Student, Department of English

THE CHALLENGE

1. Defining

2. Aggregating

3. Preprocessing 4. Analyzing

5. Postprocessing

THE PROCESS

If text analysis is to participate in literary critical endeavor in some manner beyond fact-checking, it must endeavor to assist the critic in the unfolding of interpretive possibilities. [. . .] The evidence we seek is not definitive, but suggestive of grander arguments and schemes.

Stephen Ramsay

Interpret ive Poss ib i l i t ies

DEFINING

The Data Set AGGREGATING

metadata (xml) text (txt)

PREPROCESSING CLEANING TEXT

PREPROCESSING PREPPING FOR MAXIMUM INTERPRETATION

 Learn from seen (large) data  Patterns, Properties, etc.

 Perform well on unseen (large) data  Predict  Decide  Classify

 “Torture data” just right

What is Machine Learning?

ANALYZING

As s imple as poss ib le but not s impler.

MACHINE LEARNING

In

I2 I1

thm

ori

Alg

Machine Learning

PROCESS

Choose the methods

Feed the data

Take decisions, make predictions

Toolbox Define space of possible models

Learn structure and the parameters of the model

Take decisions, make predictions

Modeling

Categor izat ion

MACHINE LEARNING

• Input • Some output • Learn a model • Prediction, Decision

Supervised

• Input • Learn patterns using a model • Prediction, Decision

Unsupervised

• Input • Grading of some output • Prediction, Decision

Reinforcement Learning

Labeled data

Unlabeled data

Rewarded data

Bayesian Learning

• Probabilistic models • Learn parameters

based on Bayesian rule/integration

LDA

• Generative model • Hidden/latent variables • Word, Document, Topic

triplet

Probab i l i s t i c/Bayes ian Learn ing

UNSUPERVISED LEARNING

Top ic Mode l ing

BAYESIAN LEARNING

 Find hidden (thematic) patterns/topics in documents

 Produce labels according to the patterns

 Understand, organize, search and summarize the collection of documents

S imple Idea!

PROCESS

Topic 1

Topic 3

Topic 2

Mrs Dalloway

Ulysses

house

Time

man Child

Day

Documents as a mixture of topics

LDA

Document 1

Document 2

Topics as a mixture of words

LDA

Topic 1 arabs 0.023 army 0.016 railway 0.0146 day 0.0144 ….

Topic 2 God 0.020 Father 0.0160 soul 0.0135 Sin 0.0080 Priest 0.00798 ….

Topic 3 time 0.0129 father 0.0107 Sister 0.00677 room 0.00676 House 0.0066 ….

Topics as d ist r ibut ions on words

LDA

Topic 1

Topic 2

Topic 3

Generat ive Process

LDA

For each topic Tp, a)  Choose a distribution pTp over

words For each novel Li: a)  Choose a distribution pLi over

K topics b)  for each word in Li:

i.  Draw one of the K topics, Tj, according to pLi

ii.  Draw one of the V words according to pTj

LDA Novels LDA

children room room room people house house room afternoon doctor afternoon children room house children house doctor girl afternoon house night doctor room children afternoon children room house afternoon children

Novel 1

room afternoon doctor children house afternoon night children children children children room house afternoon people doctor house house doctor room doctor afternoon doctor room room house doctor afternoon children room

Novel 2

Graphical Model

LDA

N

D

K

For B ig Modernism

LDA

LDA (mallet train-topics …)

Corpus

Filter

Topic & word Distributions

Exploration

PROCESSING STREAMLINED SCRIPT

Though the machine does a very good job in identifying the topics latent in a corpus, the machine does a comparatively poor job when it comes to auto-identifying which of the harvest topics are the most interpretable by human beings.

- Matt Jockers, Macroanalysis

Interpretat ion

POST-PROCESSING

3 SENSORY EXPERIENCE OF TEMPORALITY time felt day looked knew work face hand night

18 TIME EMBODIED eyes face life time white dark round hand head

26 INHABITING SPACES men people began room house talk suddenly end years

TOPIC INDEX

WEI

GH

T Modernist Novels

Time Embodied

Inhabiting Everyday Spaces

POST-PROCESSING Label ing Most Modernist Topics

Sensory Experience

of Temporality

Sensor y Exper ience of Temporal i ty

POST-PROCESSING

Inhabi t ing Spaces

POST-PROCESSING

Time Embodied

POST-PROCESSING

POST-PROCESSING DISTRIBUTION TABLE

  Heart of Darkness >>> Lord Jim   0.052

  Heart of Darkness >>> Secret Agent   0.809

  Lord Jim >>> Secret Agent   0.729

 Mrs. Dalloway >>> The Waves   0.586

  The Great Gatsby >>> Tender is the Night   0.132

  Autobiography of Alice B. Toklas >>> Three Lives   6.993

POST-PROCESSING AUTHOR DISTANCES

Top 10 Gener ic Modernist Novels

POST-PROCESSING

The Ambassadors Howard’s End Twilight Sleep The Captive The Good Soldier The Awakening Lord Jim The Waves Heart of Darkness

Top 10 Gener ic Modernist Novels

POST-PROCESSING

Mrs. Dalloway The Ambassadors Howard’s End Twilight Sleep The Captive The Good Soldier The Awakening Lord Jim The Waves Heart of Darkness

Top 10 Unique Modernist Novels

POST-PROCESSING

Seven Pillars The Trial Babbitt The Autobiography of Alice B. Toklas Three Lives Time Regained Nightwood 1984 Passage to India

Top 10 Unique Modernist Novels

POST-PROCESSING

Ulysses Seven Pillars The Trial Babbitt The Autobiography of Alice B. Toklas Three Lives Time Regained Nightwood 1984 Passage to India

Cluster ing

MACHINE LEARNING

 

Af f in i ty Propagat ion

CLUSTERING

Af f in i ty Propagat ion

CLUSTERING Exemplar

Cluster 1 , exemplar Tender is the Night : The Awakening (Chopin), Heart of Darkness (Conrad), Lord Jim (Conrad), The Secret Agent (Conrad), The Great Gatsby (Fitzgerald), Tender is the Night (Fitzgerald), The Trial (Kafka), Babbitt (S. Lewis), Tarr (W. Lewis), 1984 (Orwell), Burmese Days (Orwell), The Autobiography of Alice B. Toklas (Stein), Twilight Sleep (Wharton)

Cluster 2, exemplar Ulysses : Nightwood (Barnes), A Passage to India (Forster), Tess of the D’Urbervilles (Hardy), The Dubliners (Joyce), Portrait of the Artist as a Young Man (Joyce), Ulysses (Joyce)

Cluster 3, exemplar Seven Pil lars of Wisdom : Seven Pillars of Wisdom (Lawrence)

Cluster 4, exemplar Time Regained : The Ambassadors (James), The Captive (Proust), Time Regained (Proust)

Cluster 5, exemplar Mrs. Dalloway : The Good Soldier (Ford), Howard’s End (Forster), Sons and Lovers (Lawrence), Women in Love (Lawrence), Of Human Bondage (Maugham), Three Lives (Stein), The Picture of Dorian Gray (Wilde), Mrs. Dalloway (Woolf), The Waves (Woolf)

POST-PROCESSING MODERNISM SUB-CLUSTERS

Af f in i ty Propagat ion on Modernism

POST-PROCESSING

Fitzgerald Tender is the

Night The Great Gatsby

Chopin The

Awakening Conrad Heart of

Darkness Lord Jim

The Secret Agent

Lewis Tarr

Kafka The Trial

Orwell 1984

Burmese Days

Stein The

Autobiography of Alice B

Wharton Twilight Sleep

Joyce Ulysses Dubliners

Portrait of the Artist

Hardy Tess of the

D’Urbervilles

Barnes Nightwood

Forster A Passage to

India

Lawrence The Seven Pillars of Wisdom

Af f in i ty Propagat ion on Modernism

POST-PROCESSING Woolf

Mrs. Dalloway The Waves

Ford The Good

Soldier

Forster Howard’s

End

Lawrence Sons and

Lovers Women in

Love

Stein Three Lives

Wilde The Picture of Dorian

Gray

Proust Time Regained

The Captive

James The

Ambassadors

Fitzgerald Tender is the

Night The Great Gatsby

Chopin The

Awakening

Conrad H. of D.

Lord Jim Secret Agent

Ford The Good

Soldier

Forster Howard’s End

James The

Ambassadors

Kafka The Trial

Lawrence The Seven

Pillars Lawrence Sons and

Lovers Women in

Love

Lewis Tarr

Lewis Babbitt

Maugham Of Human Bondage

Orwell 1984

Burmese Days

Proust Time

Regained The Captive

Stein The Autobio

of Alice Three Lives

Wharton Twilight Sleep

Joyce Ulysses Dubliners

Portrait of the Artist

Barnes Nightwood

Hardy Tess of the

D’Urbervilles

Forster A Passage

to India

Wilde The Picture of Dorian

Gray

Woolf Mrs.

Dalloway The Waves

10 GIFTS and SOCIETY give felt time father mind people speak present poor 26 EMBODIED TIME time face hand eyes day looked place house turned 17 FAMILY RELATIONS sister family mother friend manner feelings time morning father 27 BODIES IN SPACE night half round room eye eyes sat heard head

TOPIC INDEX

WEI

GH

T 19th-Century Novels

Sensor y Exper ience of Temporal i ty

POST-PROCESSING

Family

Relations

Embodied

Time

Gifts and Social

Relations

Cluster 1 , exemplar Emma (Austen):

Emma (Austen), Pride and Prejudice (Austen), Persuasion (Austen) Cluster 2, exemplar Vil lette (Brontë):

Jane Eyre (Brontë), Villette (Brontë) Cluster 3, exemplar A Tale of Two Cities (Dickens):

A Tale of Two Cities (Dickens) Cluster 4, exemplar Great Expectations (Dickens):

Great Expectations (Dickens), Oliver Twist (Dickens), North and South (Gaskell), The Portrait of a Lady (James), The Turn of the Screw (James)

Cluster 5, exemplar Middlemarch (Eliot) : Middlemarch (Eliot), Silas Marner (Eliot), The Mill on the Floss (Eliot), The Return of the Native (Eliot)

POST-PROCESSING MODERNISM SUB-CLUSTERS

47 TIME EMBODIED face eyes time hand head night house turned looked 18 WOMEN, TIME, AND SPACE time day mind room woman life years place heard 8 SENSORY INTERPRETATION

felt sense fact question face life present eyes light

TOPICS

WEI

GH

T Modernism and 19th-Century Novels

Cluster 1 , exemplar Tender is the Night :   Nightwood, The Awakening, Heart of Darkness, Lord Jim, The Great Gatsby, Tender is

the Night, Babbitt, Tarr, 1984, The Captive, Time Regained, The Autobiography of Alice B. Toklas, Twilight Sleep

Cluster 2, exemplar A Passage to India :   The Good Soldier, A Passage to India, The Trial, Burmese Days

Cluster 3, exemplar Ulysses :   The Secret Agent, Dubliners, Portrait of the Artist, Ulysses, North and South

Cluster 4, exemplar Seven Pi l lars of Wisdom :   Seven Pillars of Wisdom

Cluster 5, exemplar Mrs. Dal loway :   Howard’s End, Women in Love, Sons and Lovers, Of Human Bondage, Three Lives, The

Picture of Dorian Gray, Mrs. Dalloway, The Waves Cluster 6, exemplar Emma :

  Emma, Persuasion, Pride and Prejudice Cluster 7, exemplar Great Expectations:

  A Tale of Two Cities, Great Expectations, Oliver Twist Cluster 8, exemplar Silas Marner :

  Tess of the D’Urbervilles, Middlemarch, Silas Marner, The Mill on the Floss, Return of the Native

Cluster 9, exemplar The Turn of the Screw :   The Ambassadors, The Turn of the Screw, Jane Eyre, Villette, The Portrait of a Lady,

The Turn of the Screw

POST-PROCESSING MODERNISM AND 19th-CENTURY SUB-CLUSTERS

Cluster 1 , exemplar Tender is the Night : The Awakening, The Great Gatsby, The Good Soldier, Howard’s End, A Passage to India, The Ambassadors, The Trial, Seven Pillars of Wisdom, Women in Love, Babbitt, Tarr, Of Human Bondage, 1984, Burmese Days, The Captive, Time Regained, The Autobiography of Alice B., Three Lives, Emma, Persuasion, Pride and Prejudice, Middlemarch, The Mill on the Floss, The Portrait of a Lady, The Turn of the Screw

Cluster 2, exemplar Ulysses : Nightwood, Heart of Darkness, Lord Jim, The Secret Agent, Tess of the D’Urbervilles, Dubliners, Portrait of the Artist, Sons and Lovers, Twilight Sleep, The Picture of Dorian Gray, Mrs. Dalloway, The Waves, Jane Eyre, Villette, A Tale of Two Cities, Great Expectations, Oliver Twist, Silas Marner, North and South, Return of the Native

POST-PROCESSING MODERNISM AND 19th-CENTURY SUB-CLUSTERS

Myster y Novels INFERENCE

Given the topics, compute the topic distribution of a new novels

Infer the closest Novels (KL distance)

Example: Given X.txt, Y.txt, and Z.txt novels, find the closest novel

Prof i l ing the texts

INFERENCE

Mystery Texts

X Y Z

Mystery Texts, Topic Inference

X Ulysses-0.776

Nightwood–1.098 Mrs. Dalloway–1.107

Portrait-1.251 The Waves-1.358

Mrs. Dalloway–0.8 The Waves-0.925

Ulysses–0.934 Nightwood–1.032

Heart of Darkness–1.237

Y The Secret Agent-0.47

Tarr–0.686 Lord Jim–0.85

Heart of Darkness–0.908 Ulysses–1.153

The Secret Agent–0.385 Lord Jim–0.7 Tarr–0.741

Heart of Darkness–0.762 Turn of the Screw–1.164

Z Tender is the Night–1.375

Dubliners-1.566 Babbitt-1.588 The Trial-1.6 Tarr-1.663

The Trial-1.046 Babbitt-1.908

Sons and Lovers-1.918 Tarr-2.116

Tender is the Night-2.142

INFERENCE Simi lar i ty to other novels

Modernist Versions Project Compute Canada

Maker Lab in the Humanities

Contact info: Belaid Moa

[email protected] Jana Millar Usiskin

[email protected] @jana_mu

THANK YOU

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