Bestseller Analysis: Visualization Fiction (for PyData Boston 2013)

Bestseller Analysis:
Visualizing Fiction
Lynn Cherny

@arnicas

PyData Boston 2013

Language, Sex,Violence
(also spoilers)
TEXT

THEVIDEO OF THAT TALK:
http://blogger.ghostweather.com/2013/06/analysis-of-ﬁction-
my-openvisconf-talk.html

http://www.youtube.com/watch?
v=f41U936WqPM

BASED ON A PREVIOUS
TALK:
This talk focuses on some more technical details and more on topic analysis.

The IPython notebook of code samples for this lives here:

http://ghostweather.com/essays/talks/openvisconf/Pydata_Code.ipynb

http://www.economist.com/blogs/graphicdetail/2012/11/fifty-shades-data-visualisations
BY

Text Classification (Commonly)
§ “Bag of words” – each document is considered
a collection of words, independent of order

§ Frequencies of certain words are used to
identify the texts

Seems like this should work with sex scenes,
right? Only so many body parts and behaviors,
right?!

Data

Label

Estdsgfd fdsatreatret dfds

Yes

Dsrdsf drerear ewrewtrew

No

Reret retdrtd rewrewrtew

Yes

Dsfgdg fdsfd

Yes

Algorithm
Train

Test

New data in the wild

Sex Scene Detection First Steps
1.  Buy 50 Shades on Amazon, unlock text in
Calibre, save as TXT ﬁle.

2.  Cut up a doc into 500 “word” chunks using
Python

“Would you like to sit?” He waves me toward an L-shaped white leather couch.

His office is way too big for just one man. In front of the floor-to-ceiling windows, there’s a
modern dark wood desk that six people could comfortably eat around. It matches the
coffee table by the couch. Everything else is white—ceiling, floors, and walls, except for the
wall by the door, where a mosaic of small paintings hang, thirty-six of them arranged in a
square.They are exquisite—a series of mundane, forgotten objects painted in such precise
detail they look like photographs. Displayed together, they are breathtaking.

“A local artist.Trouton,” says Grey when he catches my gaze.

“They’re lovely. Raising the ordinary to extraordinary,” I murmur, distracted both by him
and the paintings. He cocks his head to one side and regards me intently.

“I couldn’t agree more, Miss Steele,” he replies, his voice soft, and for some inexplicable
reason I find myself blushing.

Sample of 50 Shades of Grey

Manual labeling suckage
http://www.deargrumpycat.com/

http://www.ebay.com/itm/Adult-Sex-Toys-Tools-Handcuffs-Eye-mask-Neck-Band-Strap-Whip-Rope-/330845727274?pt=
UK_Home_Garden_Celebrations_Occasions_ET&hash=item4d07f12a2a

Sexually Exxxplicit,
but still a
http://www.icts.uiowa.edu/sites/default/ﬁles/contract.jpg

How’d the raters do?
Sex Scenes
Steamy Scenes

Comparing to “Pornographic”…

On to the learning algorithm…
So, the training data:

- The text chunks

- The score the raters gave it (averaged) as “truth”

I started with Python’s NLTK (Natural Language
Toolkit) and Naïve Bayes for classifying (working
in an ipython notebook).

Resources on NLTK Naïve Bayes
§ The NLTK book chapter:
http://nltk.googlecode.com/svn/trunk/doc/
book/ch06.html

§ Jacob Perkins’ example of sentiment analysis
with NLTK:
http://streamhacker.com/2010/05/10/text-
classiﬁcation-sentiment-analysis-naive-bayes-
classiﬁer/

Perkins’ NLTK code for this…
import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
def word_feats(words):
return dict([(word, True) for word in words])
negids = movie_reviews.fileids('neg')
posids = movie_reviews.fileids('pos')
negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids]
negcutoff = len(negfeats)*3/4
poscutoff = len(posfeats)*3/4
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print 'train on %d instances, test on %d instances' % (len(trainfeats),
len(testfeats))
classifier = NaiveBayesClassifier.train(trainfeats)
print 'accuracy:', nltk.classify.util.accuracy(classifier, testfeats)
classifier.show_most_informative_features()

His movie sentiment output
72% accuracy, trained on 1500 inputs.

My results on 50 Shades sex
Scenes
82 % accuracy!

Previously with less “pos” data: not so
great at 68%
“packet” (they use a lot of condoms)

Python’s sklearn (scikit-learn)
Lots of classiﬁers

for sparse data like
text!

http://scikit-learn.org/0.13/auto_examples/
document_classiﬁcation_20newsgroups.html

Using a lemmatizer step in the pipeline (to strip endings off words, since some ﬁction in my
later samples was in present tense)

Pipelines in sklearn makes it incredibly easy to run lots of experiments.

Fit the model, using training data and “target” answers (in this case,“50 Shades of Grey”)

Test the model on new data (in this case,“50 Shades Darker”). Check how it did against the
answers.

Now
we’re
at 88%

Interpreting the results…
Let’s make a tool!
Demo:
http://www.ghostweather.com/essays/talks/openvisconf/text_scores/
rollover.html

Really amazing P.S. here…
I paid for coding of a bunch of fan-fiction for sex
scenes too, and fed them in to the sklearn SGD
classifier.

(Note that 50 Shades started life as Twilight
fanfic.)

*cross-validating with entire set, not just 50 Shades books.

97% accuracy achieved!*

TOPIC ANALYSIS
Moving on to Dan Brown!

Almost naked, Silas hurled his pale body down the staircase. He knew he
had been betrayed, but by whom? When he reached the foyer, more
officers were surging through the front door. Silas turned the other way
and dashed deeper into the residence hall.The women's entrance. Every
Opus Dei building has one.Winding down narrow hallways, Silas snaked
through a kitchen, past terrified workers, who left to avoid the naked
albino as he knocked over bowls and silverware, bursting into a dark
hallway near the boiler room. He now saw the door he sought, an exit light
gleaming at the end.
Running full speed through the door out into the rain, Silas leapt off the
low landing, not seeing the officer coming the other way until it was too
late.The two men collided, Silas's broad, naked shoulder grinding into the
man's sternum with crushing force. He drove the officer backward onto the
pavement, landing hard on top of him.The officer's gun clattered away.
Silas could hear men running down the hall shouting. Rolling, he grabbed
the loose gun just as the officers emerged. A shot rang out on the stairs,
and Silas felt a searing pain below his ribs. Filled with rage, he opened
fire at all three officers, their blood spraying.
A dark shadow loomed behind, coming out of nowhere.The angry
hands that grabbed at his bare shoulders felt as if they were infused with
the power of the devil himself.The man roared in his ear. SILAS, NO!
Silas spun and fired.Their eyes met. Silas was already screaming in
Chapter 96
DaVinci Code

Resources for Topic Analysis
§ David Mimno’s java Mallet is “the one everyone
uses”:

- http://mallet.cs.umass.edu/index.php

- The R mallet package is rather nice, too:
http://www.cs.princeton.edu/~mimno/R/

- This is a GUI wrapper for mallet that outputs nice csv
and html pages:
https://code.google.com/p/topic-modeling-tool/

§ Some pure python (and C) implementations (toy
code, primarily) are listed on Blei’s website:
http://www.cs.princeton.edu/~blei/
topicmodeling.html

Pros/Cons vs CMD-Line Mallet
Pros

§  Allows stopword ﬁle
specifying

§  Produces csv and html
output in a near dir
structure

§  Has a GUI (simpler to just
get going)

Cons

§  Runs with defaults, so no
optimize-interval or other
cmd line options

§  No diagnostic output (a
command-line option)

§  Not super-well doc’d

Tutorial on cmd line usage:
http://programminghistorian.org/lessons/topic-modeling-and-
mallet

My notebook has lots of code to
process these files…

A few pandas stats…
107 chapters, 10 topics “requested”…
Topic proportion distribution…

The default HTML output is a little
lacking…
A bipartite graph of chapters and topics is an
obvious vis method….

Making the objects:
Make objects of nodes, links, and any extra data
values on each that you want…

Improving the network’s UI…
Adding strength, highlight effect, another variable, and informative tooltips.

Demo:

http://
www.ghostweather.com
/essays/talks/
openvisconf/
topic_docs_network/
index_better.html

Maybe I need One More Tool. Any word relations of interest?
Let’s try another hairball…
Demo: http://www.ghostweather.com/essays/talks/openvisconf/topic_words_network/index.html

Small
“constellations”
show shared
words (an
accident that’s
useful!)

Filtered to only the
“exciting” nodes…

Another tool:

DaVinci Code topics to
chapters mapping

“Excitement” rating color scale
avg by chapter, ordered
(obviously)

Topics (48ish) per
chapter (108)

Chapter 1… to Chapter 108

Ah, but since it’s svg/d3…

var chart = chart.append("g").attr("translate","0," +
y).attr("transform","rotate(90 600 600)");

But, maybe I need chapter
summaries…. So I can relate
them to the topics?

Add some topic-tooltips
and fade-outs….

Demo: http://www.ghostweather.com/essays/talks/openvisconf/topic_arc_diagram/TopicArc.html

But what did this
show?
Some topics are just neither exciting nor
dull – topic clustering (as I did it) had little
to do with action scenes. It’s slightly helpful
for topics, though J

These nodes are shaded from
gray (dull) to red (exciting)

Coming soon…
Color words in texts by topic assigment, to help
tune the stopwords and set up next steps:
•  Pre-process text for just the verbs?
•  Clean out a class of proper names
•  Extract sentences containing the topic words
to help describe the topics/texts better

Wrapping up…
§ Python is great for the data munging and
analysis

§ Some analysis needs serious vis support

§ Save yourself some work in javascript using
Python before you get into js

§ D3 is a great tool for iterative interactive
exploration of your analysis results

THANKS!
@arnicas, Lynn@ghostweather.com

My thanks to….

Luminosity for help with Dan Brown summaries, JimVallandingham (@vlandham)
for network parameter and coffeescript help.

Hey, I am a consultant for data analysis and visualization. Look me up!

A Few More References
§  Applied Machine Learning with Scikit-Learn:
http://scikit-learn.github.io/scikit-learn-tutorial/index.html

§  Naïve Bayes for text in Scikit-Learn:
http://scikit-learn.org/stable/modules/naive_bayes.html#naive-bayes

§  Stochastic Gradient Descent in Scikit-Learn: http://scikit-learn.org/0.13/modules/sgd.html

§  Nice tutorial overview of working with text data:
scikit-learn.github.io/scikit-learn-tutorial/working_with_text_data.html

§  Bearcart by Rob Story – Rickshaw timeseries graphs from python pandas datastructure in 4
lines (https://github.com/wrobstory/bearcart)

§  LDA topic modeling tool with UI - https://code.google.com/p/topic-modeling-tool/

§  Scott Weingart’s nice overview of LDA Topic Modeling in Digital Humanities:
http://www.scottbot.net/HIAL/?p=221

§  Elijah Meeks’ lovely set of articles on LDA & Digital Humanties vis:
https://dhs.stanford.edu/comprehending-the-digital-humanities/

§  JimVallandingham’s tooltip code and a great demo/tutorial:
http://ﬂowingdata.com/2012/08/02/how-to-make-an-interactive-network-visualization/

§  Rickshaw for timeseries graphs: https://github.com/shutterstock/rickshaw

Bestseller Analysis: Visualization Fiction (for PyData Boston 2013)

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