Jellysmack's mission is to detect and develop the most talented video Creators through technology. In early stages, we detected creators who became very successful on Facebook and we decided to launch more creators doing similar content. But how to find those creators when there's no precise categories on social media ? In this talk, you'll find out what text-based approaches we tried on titles and tags to create our own in-house categorization system and how we continuously get user feedback feeding our models.

1. Empower Creators with IA
Paris WiMLDS - 29/11/2021

3. Jellysmack detects and develops the
world’s most talented video Creators
through technology.
We are the only company building the hyper-engaged communities
that every Creator dreams of.
We optimize and distribute video content across social platforms
allowing Creators to reach authentic new fans with zero effort.
Through the power of our data, we maximize reach and revenue so our
Creators can stay focused on their passion—creating the best content.
Jellysmack unlocks each creator’s full potential.

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Genesis of Jellysmack
TV is being replaced by social media as
the new source of entertainment

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So let’s create thematic channels on
social networks and on several of them!
and many more

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Let’s create content but let’s make sure our content will be successful!
That’s when the Data came into play:

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Jellysmack Creator
10B
views / month
500M
reached / month

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Expertise + AI tools
to serve Creators!
Our goal : to optimize and distribute
content cross-platform in order to
reach a bigger audience.
The challenge : every platform is
different and each content has to
adapted accordingly.
Engagements: 10sec.
16x9
Engagements: 3sec.
4x3
Youtube Facebook
Original video
Logging
Reframing
Editing
...

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First success: Reaction videos
The start of a Data Science journey to business value
First problem: How to find more of them when there’s no precise categories on social media platforms?

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Proprietary
Private
Public
Data coming from platforms on our own channels
Data enriched and generated by Jellysmack
Data directly accessible from the platforms
Data at heart

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A text classification based approach
Why Text?
Data at our hand
● Title
● Tag
● Comments
● Thumbnail
● Video

14. Human to the rescue

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Dataset

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DATASET

17. EXPERIMENT
● TF-IDF
● CNN
● MG-CNN
● ULMFiT
● BERT

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A text classification based approach : TF-IDF
● Bag of words
● Logistic Regression
● Millions of videos = huge dimension (V > 10k)

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A text classification based approach : CNN MG-CNN

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A text classification based approach : ULMFiT
○ Pre-trained LM
○ Transfer Learning
○ Target dataset fine tuning

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A text classification based approach : BERT
○ Became state-of-the-art at that time
○ Masked Language Model
○ Bi-directional model

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RESULTS
Accuracy
Time with CPU 8 Cores and Size Trained Models
Reality check !
BERT is doing well but at what cost ...

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CNN
Best compromise
Accuracy / Speed / Memory
CONCLUSION
Tags
Better than Titles

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How we enhanced our product to get continuous end-user feedback.
Data Product

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ReactionHacks
Reaction Time
AzzyLand
4000+
discovered
35
signed
Business Impacts & Benefits
4M$
revenue
12B
views

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2
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Human expertise powering Data products
Data
Science
Human
Expertise
Cross-Platform Obsession

29. Interested in joining the adventure?
Check our opportunities online!