some thoughts on computational narratologysome thoughts on computational narratology kristo er l...

Some Thoughts on Computational NarratologyDynamic Evolution and Compositional Change in Literature

Kristoffer L [email protected]

knielbo.github.io

Dept. of History & SDU eScience CenterUniversity of Southern Denmark

Some Thoughts onComputational

Narratology


knielbo.github.io

Automatedmicro-analysis

DH Revisited

Narrative

Narrative Coherence

Dynamic evolution ofsentiment

Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change

Compositional changedetection

Lexical changedetection

Topical distances

Model dynamics

outline

1 Automated micro-analysisDH RevisitedNarrative

2 Narrative CoherenceDynamic evolution of sentimentStory arcHurst estimationGlobal coherenceLocal coherenceProposalTowards scalability

3 Narrative ChangeCompositional change detectionLexical change detectionTopical distancesModel dynamics


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

dh revisited

Learning to walk before we run

“In humanities research, the use of data analytics and high perfor-mance computing is advanced under the banners of ‘distant reading’and ‘macroanalysis’. These technologies are supposed to give us en-tirely new insights that have previously been unobtainable. The resultshowever often resembles technical demonstrations rather than solutionsto research problems. In order to really benefit from analytics and HPC,we first need to operationalize and automate microanalysis.”


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

narrative

- A narrative is a sequence of intentionally dependent events (‘objects bounded in time’)

directed at some goal-state

- [example] An action (perception of) has a narrative structure, the success of which

depends on the (causal) coherence between the sub-actions and intended goal

Figure 1: Partonomy of ‘drinking beer’

Capture a narrative’s evolution (perception of) by focusing on the coherence ofaffective dynamics and co-occurrence structure of one text


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

Data

- Kazuo Ishiguro’s Nobel-prize winning Never Let Me Go (2005) which isdriven by a “great emotional force”

- Sentence-level sentiment estimation based on the Syuzhet lexicon

Problem

- Psychological/affective experience of a narrative

- Aesthetics optimality for literary fiction

Hu, Q., Liu, B. Thomsen, M.R., Gao, J. & Nielbo, K.L. (in review). Dynamic evolution of sentiments in Never Let MeGo: Insights from multifractal theory and its implications for literary analysis.


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

filtered story arc

0 1000 2000 3000 4000 5000−5

0

5

Time

Se

ntim

en

t

Original

Number of Sentences =5526

(a1) Original t = L/200 t = L/15

0 1000 2000 3000 4000 5000−1

−0.5

0

0.5

1

Time

Se

ntim

en

t va

lue

(a2)

filitered(t = L/15)filitered(t = L/4)

0 1000 2000 3000 4000 5000−0.5

0

0.5

1

Time

Se

ntim

en

t

Normalization

Number of Sentences =5526

(b1)Original t = L/200 t = L/15

0 1000 2000 3000 4000 5000−1

−0.5

0

0.5

1

Time

Se

ntim

en

t va

lue

(b2)

filitered(t = L/15)filitered(t = L/4)

Figure 2: Sentiment time series of Never Let Me Go


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

Figure 3: Computation of local fluctuationsaround linear, quadratic, and cubic trends Figure 4: Estimation of Hurst parameter


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

global coherence

2 4 6 8 10−2

−1

0

1

2

Hs = 0.6072 ± 0.0062

Hl = 0.3306 ± 0.012

log2w

log

2F

(w)

(a) Orginal series

2 4 6 8 10

−5

−4

−3

−2

−1

Hs = 0.6079 ± 0.0076

Hl = 0.3623 ± 0.0268

log2w

log

2F

(w)

(b) Normalized series

Figure 5: The Hurst parameters of original and normalization sentiment time series ofNever Let Me Go


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

local coherence

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500

0.5

0.55

0.6

0.65

0.7

0.75(a)

Time

Hu

rst

Original seriesfiltered(t = L/60)

500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500

0.5

0.55

0.6

0.65

0.7

0.75

a bc

d ef

g

h

i

j

(b)

Time

Hu

rst

Normalized seriesfiltered(t = L/60)

Figure 6: The evolution of Hurst under 256 window size of original and normalizedsentiment time series


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

- The (global) Hurst exponent of a novel’s sentiment story arc provides anindex of a novel’s narrative coherence. This index can be used as anevaluation metric of how the novel’s moods, feelings and attitudes will beperceived by a reader.

- As an evaluation metric, the Hurst exponent of a novel can be interpretedaccordingly: 0.5 < H < 1 indicates a coherent narrative; H = 0.5indicates a narrative that is incoherent, almost random; and H < 0.5indicates a overly rigid and potentially bland narrative.

- the optimal narrative manages the reader’s motivation by neither beingcompletely coherent (H ≈ 1) nor incoherent (H = 0.5), but somewhere inbetween.

- For H > 0.5, the (local) time-varying Hurst exponents reflects variationin the novel’s plot, such that local minima reflect disruptions or points ofnarrative change, positive incline reflect continuous (persistent) narrativedevelopment, and decline a movement towards disruptions.


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

Figure 7: global H for Danish textual cultural heritage


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

Data

- Saxo Grammatricus (c. 1160 - post 1208) represents the beginning of themodern day historian in Scandinavia

- Gesta Danorum (“Deeds of the Danes”) is the single most importantwritten source to Danish history in the 12th century

Problem

- bipartite composition of Gesta Danorum

- is the transition between the old mythical and new historical part locatedin book eight, nine, or ten

- is this transition gradual (continuous) or sudden (point-like)

- qualitative observations and contextual knowledge to argue for aparticular change in content and composition

Nielbo, K.L., Perner, M.L., Larsen, C., Nielsen, J. & Laursen, D. (in review). Change Detection in Gesta Danorum’sTopical Composition


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

lexical change detection

Figure 8: Most frequent keywords and entities in Gesta Danorum in windows of 50sentences


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

topical distances

Figure 9: Cosine distance matrix for vector space model and relative entropy betweendocuments in seeded topic model of Saxo


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

model dynamics

Figure 10: Model dynamics


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

summary

- Gradual transition that starts in the latter part of book eight and ends inbook ten

- greatest rate of change in book nine, which explains the point-likeposition

- using co-occurrence structure of a document show superior results incomparison to classical VS model


Narratology


knielbo.github.io


DH Revisited

Narrative

Narrative Coherence


Story arc

Hurst estimation

Global coherence

Local coherence

Proposal

Towards scalability

Narrative Change



Topical distances

Model dynamics

THANK YOU

[email protected]

knielbo.github.io

slides: http://knielbo.github.io/files/kln narratology.pdf

& credits toQiyue Hu, Bin Liu & Jianbo Gao, Institute of Complexity Science and Big Data, Guangxi

University, CHN

Mads Rosendahl Thomsen, Institute for Comparative Literature, Aarhus University, DK

Ditte Laursen, Royal Danish Library, DK

& fundingDanish Agency for Science and Innovation: Calculus of Culture

Andrew Mellon Foundation: Mapping Literary Influences

some thoughts on computational narratologysome thoughts on computational narratology kristo er l...

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