PyData London - 47th Meetup Pushkal Agarwal

How Do People Engage With Parliamentary Debates?
Pushkal Agarwal1, Nishanth Sastry1 , Edward Wood2
1King’s College London, London, 2UK Parliament
1
6 Million Views
In two years
How can you engage?
• Parliamentary News from the Horse’s
Mouth.
• Search, Share and Embed the content.
• Read at https://hansard.parliament.uk/
or Watch Live/Archived at
https://parliamentlive.tv/
2
How People Engage?
“Personas” Using Unsupervised learning
•NMF: Non-negative
Matrix Factorisation
•V~WH (k=3,
optimum)
3
NMF algorithm from Brunet2004 (R library: “NMF”)
How People Engage?
“Components” Using Unsupervised learning
•PCA: Principal
component analysis
•Principal
components=2
(~99% data)
4
PRCOMP() method using SVD (R library: “stats”)
What Are The Engagement Patterns?
5
Social% Search% Direct%
Views 25.97 41.28 32.75
Desktop 23.97 47.95 28.08
Mobile 40.44 33.86 25.70
Tablet 28.49 44.71 26.81
Live 33.06 36.35 30.59
VoD 15.88 48.33 35.78
Archive 18.37 46.65 34.99
Average Time 154 second 245 second 137 second
Using two decomposed factored Matrices from NMF.
Thanking…
6
@edwardwood99@pushkalagarwa @nishanthsastry
Correspondence- pushkal.agarwal@kcl.ac.uk
1 of 6

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PyData London - 47th Meetup Pushkal Agarwal

  • 1. How Do People Engage With Parliamentary Debates? Pushkal Agarwal1, Nishanth Sastry1 , Edward Wood2 1King’s College London, London, 2UK Parliament 1 6 Million Views In two years
  • 2. How can you engage? • Parliamentary News from the Horse’s Mouth. • Search, Share and Embed the content. • Read at https://hansard.parliament.uk/ or Watch Live/Archived at https://parliamentlive.tv/ 2
  • 3. How People Engage? “Personas” Using Unsupervised learning •NMF: Non-negative Matrix Factorisation •V~WH (k=3, optimum) 3 NMF algorithm from Brunet2004 (R library: “NMF”)
  • 4. How People Engage? “Components” Using Unsupervised learning •PCA: Principal component analysis •Principal components=2 (~99% data) 4 PRCOMP() method using SVD (R library: “stats”)
  • 5. What Are The Engagement Patterns? 5 Social% Search% Direct% Views 25.97 41.28 32.75 Desktop 23.97 47.95 28.08 Mobile 40.44 33.86 25.70 Tablet 28.49 44.71 26.81 Live 33.06 36.35 30.59 VoD 15.88 48.33 35.78 Archive 18.37 46.65 34.99 Average Time 154 second 245 second 137 second Using two decomposed factored Matrices from NMF.

Editor's Notes

  1. Left side ; view, share and engage Massive increase in the internet 6 million live and archived views