America's Next Topic Model Lightning talk (5 mins)

Presented at Pydata London 5 July 2016

Transcript

1. America’s Next Topic Model
   Lev Konstantinovskiy
   Community Manager at Gensim
   @teagermylk
   http://rare-technologies.com/
   

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2. Streaming
   Topic Modelling and Word2vec in Python
   

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3. The questions I get asked all the time:
   - Why is your hair blue?
   - How to choose the best Topic Model?
   

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4. Business Problem solved by Topic Modelling
   Bird’s eye view of internal company documents
   Drill down into individual documents by topic.
   Rather than just keywords!
   

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5. From Latent Dirichlet Allocation paper by David M. Blei.
   Words colored according to their topic
   

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6. Colouring words in Gensim
   bow_water = ['bank','water','river', 'tree']
   color_words(goodLdaModel, bow_water)
   bank river water tree
   color_words(badLdaModel, bow_water)
   bank river water tree
   ? river bank or financial bank ?
   

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7. Automated model selection
   See "Reading Tea Leaves: How Humans Interpret Topic Models by Chang,Boyd-Graber et al".
   Model fit
   Human
   opinion
   

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8. Topic coherence = human opinion
   Coherence is how often the topic words appear ‘together’ in the
   corpus. Many ways to define ‘together’ - ‘c_v’ is the best one.
   goodcm = CoherenceModel(model=goodLdaModel, texts=texts,
   dictionary=dictionary, coherence='c_v')
   print goodcm.get_coherence()
   0.552164532134
   goodcm = CoherenceModel(model=badLdaModel, texts=texts,
   dictionary=dictionary, coherence='c_v')
   print goodcm.get_coherence()
   0.5269189184
   

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9. Summary: How to choose your next Topic Model:
   Manually:
   - Colour words
   - pyLDAVis
   Automatically:
   - Topic coherence C_v
   

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10. Why is your hair blue?
    Trying to fit in at PyCon in Portland, Oregon
    

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11. Lev Konstantinovskiy @teagermylk
    Topic coherence by our incubator
    student Devashish Deshpande
    Word colouring by our Google
    Summer of Code student
    Bhargav Srinivasa
    See you at PyCon UK Sprints!
    Monday 19 September
    

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12. Topic Model of Harry Potter
    1. (the Muggle topic) 50% “Muggle”, 25% “Dursey”, 10%
    “Privet”, 5% “Mudblood”...
    2. (the Voldemort topic) 65% “Voldemort”, 12% “Death”, 10%
    “Horcrux”, 5% “Snake”…
    3. (the Harry topic) 42% “Harry Potter”, 15% “Scar”, 7%
    “Quidditch”, 7% “Gryffindor”…
    

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13. Topic Model of Harry Potter
    Chapter 1 of Book 1: introduces the Dursley
    family and has Dumbledore discuss Harry’s
    parent’s death.
    - 40% Muggle topic
    - 30% Voldemort topic
    - 30% Harry
    

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