How Data is Transforming the Dutch Media Industry

•

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RTL Netherlands exists for 30 years in 2019. Video has been our core business. AI gives us the opportunity to deeply understand what our consumers love. On our Spark platform in AWS we apply several AI and ML methods to extract and analyze features. A selection of our content intelligence pipelines: * Object and person detection in videos. * Multi-modal emotion detection. * Speaker identification. * Script and subtitle keyword extraction. * Among others All of these features are used for different data science products: new show and episode creation, talkshow subject selection, interpret viewing ratings among others. Our future goal is to personalize TV on our video-on-demand platform. Not only recommend other series that you like, but also to create personalized talkshows and soap opera's with the subjects, storylines, guests and characters that you like. Video is this our basis, but digitally the opportunities are much more diverse. With this talk I want to inspire and share knowledge. Visiting the Spark Summit 2018, I learned a lot. Some talks even helped to further build this content intelligence project. It would be amazing to give back to the Spark community. Especially when they visit my hometown of Amsterdam. I want to surprise the attendees with the story of this unknown Dutch TV channel, that is taking a leading role on content intelligence in the Netherlands and Europe. It will be an open, inspiring talk with technical details on the pipelines and technology that we used. Accompanied with the end use cases. Including drawbacks and challenges we faced. Not a talk about ambitions, but concrete results of the next level of TV innovation. RTL NL was the first broadcaster of Big Brother and The Voice. And I'm confident that the next break-out hit will be Spark driven.

Datos y análisis

WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

How data is transforming
the Dutch media industry
Maurits van der Goes | RTL Netherlands
#UnifiedDataAnalytics #SparkAISummit

RTL NL
5
8.9 million
Daily TV viewers
LINEAIR TELEVISION
779 million
Online views per month
ONLINE VIDEO
2.3 million
unique vistors daily
DIGITAL PUBLISHING

9
Personalisation Emotion
Detection
Talkshow
Analysis
Automated
Trailers
Ratings
Forecasting

11
Domain News
Content Articles
Model Content

Taxonomy SIMILARITY TF-IDF
Embeddings
23.3%
TF.IDF
22.2%
Taxonomy
17.6%
Editor’s pick
(baseline)
Random
-19.3%
Uplift
14

15
Domain Films & Series
Content Long Video
Model Behavior

16
Explainability Neural networks Last watched A/B testing

17
30 minutes more
VIEWING TIME
per user per month

19
We tell stories
that touch the
mind & heart
MEDIA =
EMOTION

Emotion detection
20
Face& Emotion
Detection
Musical Genre &
Mood
Speaker Emotion

BERT
22
Bidirectional Encoder Representations from Transformers (Devlin et al., 2018)
google-research/bert
(Kaggle)
BERT-Base, Multilingual Cased
• 104 languages
• 12-layer
• 768-hidden
• 12-heads
• 110M parameters

FuzzyWuzzy
Levenshtein Distance
Scenario & subtitles matching
25
SubtitlesScenario
Items + TS

Item classification
26
MLlib
• Crime
• Entertainment
• Lifestyle
• Royalty
NaiveBayes 0,89946
Logistic Regression
(Count Vector Features)
0,84533
Logistic Regression
(TF-IDF Features)
0,81268
RandomForrest 0,56564
Item

Model components
–0.12 pp/yr
–2.65 pp/yr
–1.95 pp/yr
Four weather variables (temperature, wind, precipitation,
sunshine), measured with respect to average conditions
for the time of year
Interactions:
• n = 1, 2 Fourier terms
• saturday/sunday
• internal
𝑓 𝑡 = $
%&'
(
𝑎% cos
2π𝑛𝑡
𝑃
+ 𝑏% sin
2π𝑛𝑡
𝑃
, 𝑃 = 1 year
Truncated Fourier series:Dummy variables for weekdays
Interaction with n = 1, 2 Fourier terms
Piecewise-linear functionDummy variables for various “events” (e.g. holidays,
various sports events)
Components: trendComponents: trend + seasonalComponents: trend + seasonal + weekdayComponents: trend + seasonal + weekday + weatherComponents: trend + seasonal + weekday + weather
+ event
29

Overall Statistics
Manual Data Reduction
Mean Error 0.93 0.29 -68%
Mean
Percentage
Error
5.4% 1.3% -77%
Mean
Absolute
Error
1.63 1.27 -22%
Mean
Absolute
Percentage
Error
9.5% 7.3% -23%
30

Why PROMO GENERATION?
Promos are ideal for building awareness, branding and teasing the
audience.
Decline in viewership per channel -> increase in channels &
promos.
Promo generation is costly, a highly labor-intensive process.
Artificial Intelligence can aid in analyzing, selecting an combining
potential candidate shots and scenes.
32

Video
Aesthetics
Optical Flow
Shot
Segmentation
Key Frame
Extraction
Object Detection,
Recognition &
Segmentation
Face Detection,
Recognition &
Emotion
Visual Similarity
Feature extraction
(Pachyderm)
33
Features

37
What WhenWhere
Personalisation Emotion
Detection
Talkshow
Analysis
Automated
Trailers
& moreRatings
Forecasting

DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

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1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. How data is transforming the Dutch media industry Maurits van der Goes | RTL Netherlands #UnifiedDataAnalytics #SparkAISummit

4. 4

5. RTL NL 5 8.9 million Daily TV viewers LINEAIR TELEVISION 779 million Online views per month ONLINE VIDEO 2.3 million unique vistors daily DIGITAL PUBLISHING

6. 6 Consumers Content What When Where

7. 7

8. 8

9. 9 Personalisation Emotion Detection Talkshow Analysis Automated Trailers Ratings Forecasting

10. 10 Personalisation

11. 11 Domain News Content Articles Model Content

12.

13.

14. Taxonomy SIMILARITY TF-IDF Embeddings 23.3% TF.IDF 22.2% Taxonomy 17.6% Editor’s pick (baseline) Random -19.3% Uplift 14

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19. 19 We tell stories that touch the mind & heart MEDIA = EMOTION

20. Emotion detection 20 Face& Emotion Detection Musical Genre & Mood Speaker Emotion

21. 21 oarriaga/face_classification

22. BERT 22 Bidirectional Encoder Representations from Transformers (Devlin et al., 2018) google-research/bert (Kaggle) BERT-Base, Multilingual Cased • 104 languages • 12-layer • 768-hidden • 12-heads • 110M parameters

23. 23

24. 24 Talkshow Analysis

25. FuzzyWuzzy Levenshtein Distance Scenario & subtitles matching 25 SubtitlesScenario Items + TS

26. Item classification 26 MLlib • Crime • Entertainment • Lifestyle • Royalty NaiveBayes 0,89946 Logistic Regression (Count Vector Features) 0,84533 Logistic Regression (TF-IDF Features) 0,81268 RandomForrest 0,56564 Item

27. 27

28. 28 Ratings Forecasting

29. Model components –0.12 pp/yr –2.65 pp/yr –1.95 pp/yr Four weather variables (temperature, wind, precipitation, sunshine), measured with respect to average conditions for the time of year Interactions: • n = 1, 2 Fourier terms • saturday/sunday • internal 𝑓 𝑡 = $ %&' ( 𝑎% cos 2π𝑛𝑡 𝑃 + 𝑏% sin 2π𝑛𝑡 𝑃 , 𝑃 = 1 year Truncated Fourier series:Dummy variables for weekdays Interaction with n = 1, 2 Fourier terms Piecewise-linear functionDummy variables for various “events” (e.g. holidays, various sports events) Components: trendComponents: trend + seasonalComponents: trend + seasonal + weekdayComponents: trend + seasonal + weekday + weatherComponents: trend + seasonal + weekday + weather + event 29

30. Overall Statistics Manual Data Reduction Mean Error 0.93 0.29 -68% Mean Percentage Error 5.4% 1.3% -77% Mean Absolute Error 1.63 1.27 -22% Mean Absolute Percentage Error 9.5% 7.3% -23% 30

31. 31 Automated Trailers

32. Why PROMO GENERATION? Promos are ideal for building awareness, branding and teasing the audience. Decline in viewership per channel -> increase in channels & promos. Promo generation is costly, a highly labor-intensive process. Artificial Intelligence can aid in analyzing, selecting an combining potential candidate shots and scenes. 32

33. Video Aesthetics Optical Flow Shot Segmentation Key Frame Extraction Object Detection, Recognition & Segmentation Face Detection, Recognition & Emotion Visual Similarity Feature extraction (Pachyderm) 33 Features

34. MAN OR MACHINE?

35. Man or Machine?MAN OR MACHINE?

36. MAN OR MACHINE?

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How Data is Transforming the Dutch Media Industry

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