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Visualzing Topic Models

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Turi, Inc.
Turi, Inc.

Presenter: Ben Mabey, SavvySherpa

Technology
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Visualizing Topic Models Ben Mabey @bmabey
2
2 Latent Dirichlet Allocation (LDA)
2 0 1 … k doc a 0.25 0.14 … 0.02 doc b 0.01 0.30 … 0.09 … … … … 0.31 doc D 0.13 0.07 … 0.01 Document-Topic Distributions Latent Dirichlet Allocation (LDA)
2 0 1 … k doc a 0.25 0.14 … 0.02 doc b 0.01 0.30 … 0.09 … … … … 0.31 doc D 0.13 0.07 … 0.01 Document-Topic Distributions 0 1 … k bird 0.002 0.01 … 0.004 coffee 0.001 0.003 … 0.009 … … … … 0.031 work 0.002 0.006 … 0.021 Term-Topic Distributions Latent Dirichlet Allocation (LDA)
3
3 250k+ stories July 2007 - May 2014
4 Game written by 14 year old passes Angry Birds as the top free iphone app
4 Topic P(T|D) 58 0.19 38 0.14 16 0.06 … … Game written by 14 year old passes Angry Birds as the top free iphone app
4 Topic P(T|D) 58 0.19 38 0.14 16 0.06 … … 58 38 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
5 Topic P(T|D) mobile apps 0.19 38 0.14 16 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps 38 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
6 Topic P(T|D) mobile apps 0.19 video games 0.14 16 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps video games 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
7 Topic P(T|D) mobile apps 0.19 video games 0.14 programming 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps video games programming app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
8 Interpreting Topic Models
8 What  is  the  meaning  of  each  topic? Interpreting Topic Models
8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? Interpreting Topic Models
8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? How  do  the  topics  relate  to  each  other? Interpreting Topic Models
8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? How  do  the  topics  relate  to  each  other? How  do  the  documents  relate  to  each  other? Interpreting Topic Models
9 What  is  the  meaning  of  each  topic?   How  prevalent  is  each  topic?   How  do  the  topics  relate  to  each  other?   How  do  the  documents  relate  to  each  other? Interpreting Topic Models
LDAvis 10 https://github.com/cpsievert/LDAvis
pyLDAvis 11 https://github.com/bmabey/pyLDAvis py
pyLDAvis 11 https://github.com/bmabey/pyLDAvis py BYOM (bring your own model)
Demo Time! 12
Distinctiveness & Saliency 13 Termite: Visualization Techniques for Assessing Textual Topic Models Jason Chuang, Christopher D. Manning and Jeffrey Heer. 2012 measure  how  much  information  a  term  conveys  about  topics
Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45
Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 15 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 16 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 17 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
Distinctiveness & Saliency 19 measure  how  much  information  a  term  conveys  about  topics…
Distinctiveness & Saliency 19 measure  how  much  information  a  term  conveys  about  topics… globally
Thank you! Learn more at http://github.com/bmabey/pyLDAvis Ben Mabey @bmabey http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/

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Visualzing Topic Models

  • 2. 2
  • 4. 2 0 1 … k doc a 0.25 0.14 … 0.02 doc b 0.01 0.30 … 0.09 … … … … 0.31 doc D 0.13 0.07 … 0.01 Document-Topic Distributions Latent Dirichlet Allocation (LDA)
  • 5. 2 0 1 … k doc a 0.25 0.14 … 0.02 doc b 0.01 0.30 … 0.09 … … … … 0.31 doc D 0.13 0.07 … 0.01 Document-Topic Distributions 0 1 … k bird 0.002 0.01 … 0.004 coffee 0.001 0.003 … 0.009 … … … … 0.031 work 0.002 0.006 … 0.021 Term-Topic Distributions Latent Dirichlet Allocation (LDA)
  • 6. 3
  • 8. 4 Game written by 14 year old passes Angry Birds as the top free iphone app
  • 9. 4 Topic P(T|D) 58 0.19 38 0.14 16 0.06 … … Game written by 14 year old passes Angry Birds as the top free iphone app
  • 10. 4 Topic P(T|D) 58 0.19 38 0.14 16 0.06 … … 58 38 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
  • 11. 5 Topic P(T|D) mobile apps 0.19 38 0.14 16 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps 38 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
  • 12. 6 Topic P(T|D) mobile apps 0.19 video games 0.14 16 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps video games 16 app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
  • 13. 7 Topic P(T|D) mobile apps 0.19 video games 0.14 programming 0.06 … … Table 2 58mobile apps 38video games 16programming app game language developer player code application video game programming user gaming java app store developer programmer mobile play programming language mobile apps video games programming app game language developer player code mobile video game programming user gaming java app store developer programmer Game written by 14 year old passes Angry Birds as the top free iphone app
  • 15. 8 What  is  the  meaning  of  each  topic? Interpreting Topic Models
  • 16. 8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? Interpreting Topic Models
  • 17. 8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? How  do  the  topics  relate  to  each  other? Interpreting Topic Models
  • 18. 8 What  is  the  meaning  of  each  topic? How  prevalent  is  each  topic? How  do  the  topics  relate  to  each  other? How  do  the  documents  relate  to  each  other? Interpreting Topic Models
  • 19. 9 What  is  the  meaning  of  each  topic?   How  prevalent  is  each  topic?   How  do  the  topics  relate  to  each  other?   How  do  the  documents  relate  to  each  other? Interpreting Topic Models
  • 24. Distinctiveness & Saliency 13 Termite: Visualization Techniques for Assessing Textual Topic Models Jason Chuang, Christopher D. Manning and Jeffrey Heer. 2012 measure  how  much  information  a  term  conveys  about  topics
  • 25. Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45
  • 26. Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 27. Distinctiveness & Saliency 14 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 28. Distinctiveness & Saliency 15 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 29. Distinctiveness & Saliency 16 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 30. Distinctiveness & Saliency 17 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 31. Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 32. Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 33. Distinctiveness & Saliency 18 coding tech news video games distinctiveness P(w) saliency game 10 10 50 0.03 0.28 0.01 apple 20 40 20 -0.16 0.32 -0.05 angry birds 1 1 30 0.25 0.13 0.03 python 50 5 10 0.17 0.26 0.05 TOTAL 81 56 110 P(T|game) 0.14 0.14 0.71 P(T|apple) 0.25 0.50 0.25 P(T|angry birds) 0.03 0.03 0.94 P(T|pyhton) 0.77 0.08 0.15 P(T) 0.33 0.23 0.45 distinctiveness weighted by the term's overall frequency computes the KL divergence between the distribution of topics given a term and the marginal distribution of topics
  • 34. Distinctiveness & Saliency 19 measure  how  much  information  a  term  conveys  about  topics…
  • 35. Distinctiveness & Saliency 19 measure  how  much  information  a  term  conveys  about  topics… globally
  • 36. Thank you! Learn more at http://github.com/bmabey/pyLDAvis Ben Mabey @bmabey http://nbviewer.ipython.org/github/bmabey/hacker_news_topic_modelling/