Data Con LA 2020 Description In this presentation we will use Machine Learning to attempt to understand stand-up comedy and predict where laughter will occur based on hidden patterns of language. Does a robot have a sense of humor, and can it laugh at your jokes? We will walk through the Data Science process – obtaining, scrubbing and exploring the data, then building a model and interpreting the results. Finally, we’ll have some fun with transfer learning. Speaker Andrew Jamieson, Disney, Executive Director

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Laughter Prediction with
Natural Language Processing

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Who Am I?
• Analytics executive with more than 20
years experience
• Entertainment, Consumer Goods,
Retail, Telecommunications,
Beverage, Snack Foods

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Successful Comedian
UnSuccessful
Comedian
Andrew Jamieson live at
Hollywood Improv 2011

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Who Should be Interested in NLP?
• Data Scientists
• Linguists
• Social Justice
• Everybody

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NLP in the News
A robot wrote this entire article. Are you scared yet, human?
– The Guardian
How GPT-3 Is Shaping Our AI Future - HBR
IBM Watson Demonstrates Natural
Language Processing Advancement
Researcher Use Natural Language Processing
Algorithms To Understand Protein Transformation
New Facebook software creates smarter natural
language processing models
Using Natural Language Processing To Improve Customer Satisfaction
The global natural language
processing (NLP) market size stood at
USD 8.61 billion in 2018 is projected
to reach USD 80.68 billion by 2026,
exhibiting a CAGR of 32.4% during
the forecast period.

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I think the set of untapped opportunities in NLP
is just massive ... So to those of you thinking of
jumping into NLP I think this is actually a very
good time” – Andrew Ng

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What is NLP? Natural Language
Processing (NLP)
• Meaning from human
language.
• Computer science,
linguistics and artificial
intelligence.
• Not all NLP is machine
learning.
artificial intelligence
machine learning
deep learning
natural language processing

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"Analyzing humor is like dissecting a frog.
Few people are interested and the frog dies of it."
– E.B. White

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Joke Structure
Premise Set Up Punchline

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Why This Will Never Work
• Shared Common Sense
• Regional / Cultural
• Tone & Body Language
• Words are 7% of meaning
• Comedy needs audience
• Language Memory

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A.I. “has the approximate computing
power of an earthworm”
– Janelle Shane (AI Weirdness)

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Why This Might Work
• Concise language
• Comedy follows a formula
• Repetitive structure
• Comedy tools can be identified
• 7% rule is misused
• Sentiment could identify persona

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"I can't think of anything worse after a night of drinking
than waking up next to someone and not being able to
remember their name, or how you met, or why they're
dead." – Laura Kightlinger)
Rule of 3’s

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Objective
To Predict Laughter
in Stand-Up Comedy

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Data Science Process
Obtain
Scrub
Explore
Model
Interpret

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Where to Get Data
• Direct access to formatted data
(Database, CSV, SQL)
• API call to get data
• Scrape the data
• Other sources (make your own)

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Web Scraping
• Web Scraping is currently legal …
• Respect /robots.txt
• Don’t take personal information
• Be considerate
• Be wary of copyright

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• 21 transcripts from George Carlin
and Dave Chappelle
• Performed between 1972 and 2019
• 19 hours of comedy
• 161,000 words
• 5,590 total laughs
About the Data
George Carlin 1975 publicity photo. Public Domain George Carlin viewable on Amazon
Dave Chappelle CC 2.0 by John Bauld (cropped to shape) Dave Chappelle viewable on Netflix and HBO

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Data Science Process
Obtain
Scrub
Explore
Model
Interpret

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Challenges
• What is a laugh?
• How to identify?
• When to tag?
• Where to tag?
• Bingeing

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NLP Preprocessing
• Tokenize
• lowercase
• Punctuation
• Stop words
• N-grams
• Stemming / Lemmatize

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Methodology
• Tag laughter on each transcript
• Separate into sentences
• Group together in rolling groups
of 3 sentence segments
• Split into individual words

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Data Science Process
Obtain
Scrub
Explore
Model
Interpret

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Word Embeddings
source: https://developers.google.com/machine-learning/crash-course
# start with father, add woman, subtract man ...
should equal mother
model.wv.most_similar(positive=['father',
'woman'], negative=['man'], topn=5)
[('wife', 0.6665226221084595),
('infection', 0.5661956071853638),
('hands', 0.558977484703064),
('pregnant', 0.5459829568862915),
('mother', 0.5429518222808838)]

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Data Summary
Laughter Count Word Count Distribution

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Transcript Visualization

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Transcript Sentiment

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Word Cloud
Word size represents the
frequency or importance
of the word.

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Topic Visualization with LDA

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Data Science Process
Obtain
Scrub
Explore
Model
Interpret

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Modeling
• Baseline Model
• Simple models first
• Recurrent Neural Network,
Long Short-Term Memory,
Gated Recurrent Units
• Convolutional Neural Network

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Measurement
• Accuracy
• Accurate + Comprehensive
• Confident Predictions

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CNN Hyperparameters
model = Sequential()
#first layer
model.add(Embedding(vocab_length, 64, input_length=100))
#10000 words, 64 dimensions, sequence length of 100 words
model.add(SpatialDropout1D(0.2))
# second layer
model.add(Conv1D(256, 3, activation='relu’))
#256 filters, 3 kernel lengths - triplets of word embeddings
model.add(GlobalMaxPooling1D())
# third layer
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.5))
# output layer
model.add(Dense(1, activation='sigmoid'))

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Model Results

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Model Results
Receiver Operating Characteristic CurvePredictions Confidence Histogram

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Model Results

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Code Demo

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Data Science Process
Obtain
Scrub
Explore
Model
Interpret

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Recommendations
• Extend the dataset
• Test different groupings
• Test impact of emotion
• Compare comedians
• Compare time periods
• Transfer learning

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Next Steps
• Extend the data
• More testing
• Transfer learning models
• Image recognition

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Questions

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Credits
p18 George Carlin 1975 publicity photo
p18 Dave Chappelle CC 2.0 by John Bauld
p20 Toddler and Steps by Jukan Tateisi on Unsplash
p21 Robot by Pete Linforth from Pixabay
p22 Laugh Woman by Jasmaine Cook from Pixabay
p24 King/Queen by Google ML Crash Course
p28 George Carlin 1975 publicity photo
p31 Yellow Wall Woman by Heather Kemp from Pixabay
p32 Laugh Woman by Heather Kemp from Pixabay
p36 Laugh Man by Jonathon Burton from Pexels
p37 Code by Johnson Martin from Pixabay
p39 Laugh sign by Tim Mossholder from Pexels
p40 Binoculars by Rudy and Peter Skitterians / Pixabay
p41 Robot toy - by Rock'n Roll Monkey on Unsplash
p41 Question - by Simone Secci on Unsplash
Images
p1 Title: Neurons by Gerd Altmann from Pixabay
p4 Data Scientist by Gerd Altmann from Pixabay
p4 Reading by PourquoiPas from Pixabay
p4 Hands by truthseeker08 from Pixabay
p6 Doors by Arek Socha from Pixabay
p8 Frog by David Clode on Unsplash
p9 Jellybeans - Image by Tariq Abro / Pixabay
p9 Setting by AbsolutVision on Unsplash
p9 Boxing Gloves by kalhh from Pixabay
p10 Worm by Catta Kvarn from Pixabay
p13 Bed by Image by Wokandapix from Pixabay
p14 Robot by StockSnap from Pixabay
p16 Laugh Man by Pexels from Pixabay
p17 Scraper by La Miko from Pexels
Fonts
Comfortaa by Johan Aakerlun
Art Brush by Gary D. Jessey

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Additional Resources
Machine Learning Mastery - several examples, here's one on LSTM
https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/
John Krohn - great resource and examples for implementing Natural Language Processing
https://github.com/jonkrohn/DLTFpT
Alice Zhao - Introduction to Text Blob for easy sentiment analysis
https://github.com/adashofdata/nlp-in-python-tutorial/blob/master/3-Sentiment-Analysis.ipynb
Bo Peng - introduction to LDAvis
https://github.com/BoPengGit/LDA-Doc2Vec-example-with-PCA-LDAvis-visualization/tree/master/LDA
Where to watch
•George Carlin - available on Amazon
•Dave Chappelle - mostly available on Netflix, some on HBO

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