This talk was given by Jana Eggink, Sam Davies and Denise Bland (BBC R&D) at the "Semantic Media @ BBC" event on 6 February 2013.

1. Mood-based Classification of
TV Programmes
Jana Eggink, Sam Davies, Denise Bland
BBC R&D
{jana.eggink, sam.davies, denise.bland}@bbc.co.uk
R&D BBC MMXIII

2. Searching the Archives
• BBC aims to open up its archives for public access by 2022
• Limited metadata available
• Title
• Broadcast date
• Genre (mostly)
• Limited: actors, semantic labels for professional use
• Mood as additional metadata, intuitive understanding
R&D BBC MMXIII

3. Which Moods?
Evaluation Potency Activity
Happy – Sad Serious – Humorous Fast paced – Slow paced
Light-hearted – Dark Exciting – Relaxing
Interesting – Boring
(EPA model based on Osgood et al, 1957)
R&D BBC MMXIII

4. User Trial
• 200 members of the general public
• 544 video clips (3 minutes excerpts)
• Each labelled by at least 6 participants
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5. Inter-rater Agreement
Do
Krippendorff’s Alpha 1
De
Agreement about Mood Labels
perfect
random
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6. Correlation
• Which moods are independent?
• Do observed correlations correspond to the EPA model?
Correlation PCA
1
light 0.9 0.6 fast
excitinginterest
0.8
fast
0.4
0.7
dark
0.6 0.2
Component 2
interest sad
serious
24% variance
0.5
0
0.4 humorous
exciting happy
0.3 -0.2
light-hearted
humorous 0.2
-0.4
0.1
relaxing
happy 0 -0.6 slow boring
happy humorous exciting interest fast light
-0.6 -0.4 -0.2 0 0.2 0.4 0.6
Component 1
63% variance
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7. Classification
Video clips
• 444 in development set, 3-fold cross validation
• 100 in holdout set
Features
• Audio (MFCCs, amplitude, zero-crossing, spectral centroid and roll-off)
• Video (face, luminance, cuts, motion)
• Genre (human assigned)
Testing
• Clips with very clear moods only
• Average rates, all clips on a 1 to 5 scale
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8. Automatic Classification Gives Good Results
Clear moods only
• 2 class problem Classification Accuracy
• >95% correct for
serious/humorous
• ~90% correct for
slow/fast-paced
R&D BBC MMXIII

9. Automatic Classification Gives Good Results
Average rates
RMS Error for detailed moods
• 1-5 scale
• ~0.7 RMSE for
serious/humorous
• <0.7 RMSE for
slow/fast-paced
R&D BBC MMXIII

10. Conclusions
• There is general agreement about mood for TV programme clips
• Mood perception is dominated by two dimensions
• Classification for clips with clear moods is very accurate, and still
possible on a detailed continuous scale
• Both genre labels and signal processing features are useful
• Humorous-serious is strongly related to genre
• Slow/fast-paced can be better modelled by audio/video features
Eggink & Bland, A Large Scale Experiment for Mood-Based Classification of TV Programmes, IEEE Int. Conf.
Multimedia and Expo, ICME2012, also as BBC White Paper Nr. 232
R&D BBC MMXIII

11. Demo
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12. Usage of the Redux Mood GUI
• Usage data 14th May 2012 to 22nd August 2012
• 3206 unique users, nearly a third (1013) are returning users
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13. Search Behaviour
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14. Programmes Watched
Frequent Programmes Watched
Never Mind the Buzzcocks 258
Torchwood 90
Dr Finlay`s Casebook 74
An Evening in with David Attenborough 55
Holiday Weatherview 49
Would I Lie to You? 46
Never Mind the Buzzcocks 36
Morecambe and Wise 33
Never Mind the Buzzcocks 32
Till Death Us Do Part 32
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15. Outliers attract Attention
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16. Outlook and Future Work
• Public facing Mood GUI based on iPlayer
• Available; http://moods.ch.bbc.co.uk
• Requires greater research in UX
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17. Outlook and future work
• Integration of pre-existing metadata
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