"Small Data" Machine Learning

What do you do if you have a single letter Twitter handle and your reply stream is virtually useless due to people using “@a” in ways that make you lose faith in humanity (“I’m @a bar” is the best of it)? These days, you might call machine learning to the rescue. ML is a hot new topic, but may be a bit difficult to get started with in a large project. This session explains how I used ML and a couple of other tricks to build a reply stream cleaner that sanitizes my Twitter consumption and will hopefully inspire you to use ML in your own projects.

Transcript

1. Small Data Machine Learning
   Andrei Zmievski
   

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2. WORK
   We are all superheroes, because we help our customers keep their mission-critical apps
   running smoothly. If interested, I can show you a demo of what I’m working on. Come find
   me.
   

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3. WORK
   We are all superheroes, because we help our customers keep their mission-critical apps
   running smoothly. If interested, I can show you a demo of what I’m working on. Come find
   me.
   

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4. MATH
   

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5. MATH
   SOME
   

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6. MATH
   SOME
   AWE
   

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7. @a
   Acquired in October 2008
   Had a different account earlier, but then @k asked if I wanted it..
   Know many other single-letter Twitterers.
   

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8. FAME
   Advantages
   

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9. FAME
   FORTUNE
   

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10. FAME
    FORTUNE
    Wall Street Journal?!
    

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11. FAME
    FORTUNE
    FOLLOWERS
    

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12. lol, what?!
    FAME
    FORTUNE
    FOLLOWERS
    

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13. 140-length(“@a “)=137
    MAXIMUM REPLY SPACE!
    

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14. CONS
    Disadvantages
    Visual filtering is next to impossible
    Could be a set of hard-coded rules derived empirically
    

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15. CONS
    I hate humanity
    Disadvantages
    Visual filtering is next to impossible
    Could be a set of hard-coded rules derived empirically
    

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16. CONS
    I hate humanity
    Disadvantages
    Visual filtering is next to impossible
    Could be a set of hard-coded rules derived empirically
    

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17. Machine Learning
    to the Rescue!
    Being grumpy makes you learn stuff
    

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18. REPLYCLEANER
    Even with false negatives, reduces garbage to where visual filtering is possible
    - uses trained model to classify tweets into good/bad
    - blocks the authors of the bad ones, since Twitter does not have a way to remove an individual tweet from the timeline
    

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19. REPLYCLEANER
    Even with false negatives, reduces garbage to where visual filtering is possible
    - uses trained model to classify tweets into good/bad
    - blocks the authors of the bad ones, since Twitter does not have a way to remove an individual tweet from the timeline
    

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20. REPLYCLEANER
    

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21. REPLYCLEANER
    

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22. REPLYCLEANER
    

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23. REPLYCLEANER
    

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25. I still hate humanity
    

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26. I still hate humanity
    I still hate humanity
    

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27. Machine Learning
    A branch of Artificial Intelligence
    No widely accepted definition
    

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28. “Field of study that gives
    computers the ability to learn
    without being explicitly
    programmed.”
    — Arthur Samuel (1959)
    

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29. SPAM FILTERING
    

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30. RECOMMENDATIONS
    

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31. TRANSLATION
    

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32. CLUSTERING
    And many more: medical diagnoses, detecting credit card fraud, etc.
    

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33. supervised
    unsupervised
    Labeled dataset, training maps input to desired outputs
    Example: regression - predicting house prices, classification - spam filtering
    

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34. supervised
    unsupervised
    no labels in the dataset, algorithm needs to find structure
    Example: clustering
    

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35. Feature
    individual measurable property of the
    phenomenon under observation
    usually numeric
    

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37. Feature Vector
    a set of features for an observation
    Think of it as an array
    

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38. features
    # of rooms
    sq. footage
    house age
    yard?
    feature vector and weights vector
    1 added to pad the vector (account for the initial offset / bias / intercept weight, simplifies
    calculation)
    dot product produces a linear predictor
    

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39. features
    102.3
    0.94
    -10.1
    83.0
    weights
    # of rooms
    sq. footage
    house age
    yard?
    feature vector and weights vector
    1 added to pad the vector (account for the initial offset / bias / intercept weight, simplifies
    calculation)
    dot product produces a linear predictor
    

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40. features
    102.3
    0.94
    -10.1
    83.0
    weights
    # of rooms
    sq. footage
    house age
    yard?
    1 45.7
    feature vector and weights vector
    1 added to pad the vector (account for the initial offset / bias / intercept weight, simplifies
    calculation)
    dot product produces a linear predictor
    

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41. features
    102.3
    0.94
    -10.1
    83.0
    weights
    # of rooms
    sq. footage
    house age
    yard?
    1 45.7
    feature vector and weights vector
    1 added to pad the vector (account for the initial offset / bias / intercept weight, simplifies
    calculation)
    dot product produces a linear predictor
    

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42. features
    102.3
    0.94
    -10.1
    83.0
    =
    weights prediction
    # of rooms
    sq. footage
    house age
    yard?
    1 45.7
    feature vector and weights vector
    1 added to pad the vector (account for the initial offset / bias / intercept weight, simplifies
    calculation)
    dot product produces a linear predictor
    

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43. X
    =
    ⇥
    1
    x1 x2 . . .
    ⇤
    ✓
    =
    ⇥
    ✓0 ✓1 ✓2 . . .
    ⇤
    dot product
    

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44. X
    =
    ⇥
    1
    x1 x2 . . .
    ⇤
    ✓
    =
    ⇥
    ✓0 ✓1 ✓2 . . .
    ⇤
    ✓
    ·
    X
    =
    ✓0 +
    ✓1x1 +
    ✓2x2 +
    . . .
    dot product
    

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45. training data
    learning algorithm
    hypothesis
    Hypothesis (decision function): what the system has learned so far
    Hypothesis is applied to new data
    

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46. hθ
    (X)
    The task of our algorithm is to determine the parameters of the hypothesis.
    

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47. hθ
    (X)
    input data
    The task of our algorithm is to determine the parameters of the hypothesis.
    

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48. hθ
    (X)
    parameters
    input data
    The task of our algorithm is to determine the parameters of the hypothesis.
    

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49. hθ
    (X)
    parameters
    input data
    prediction y
    The task of our algorithm is to determine the parameters of the hypothesis.
    

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50. LINEAR REGRESSION
    5 10 15 20 25 30 35
    40
    80
    120
    160
    200
    whisky age
    whisky price $
    Models the relationship between a scalar dependent variable y and one or more explanatory
    variables denoted X. Here X = whisky age, y = whisky price.
    Linear regression does not work well for classification because its output is unbounded.
    Thresholding on some value is tricky and does not produce good results.
    

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51. LINEAR REGRESSION
    5 10 15 20 25 30 35
    40
    80
    120
    160
    200
    whisky age
    whisky price $
    Models the relationship between a scalar dependent variable y and one or more explanatory
    variables denoted X. Here X = whisky age, y = whisky price.
    Linear regression does not work well for classification because its output is unbounded.
    Thresholding on some value is tricky and does not produce good results.
    

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52. LINEAR REGRESSION
    5 10 15 20 25 30 35
    40
    80
    120
    160
    200
    whisky age
    whisky price $
    Models the relationship between a scalar dependent variable y and one or more explanatory
    variables denoted X. Here X = whisky age, y = whisky price.
    Linear regression does not work well for classification because its output is unbounded.
    Thresholding on some value is tricky and does not produce good results.
    

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53. LOGISTIC REGRESSION
    g(z) =
    1
    1 + e z
    0.5
    1
    0
    z
    Logistic function (also sigmoid function). Asymptotes at 0 and 1. Crosses 0.5 at origin.
    z is just our old dot product, the linear predictor.
    

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54. LOGISTIC REGRESSION
    g(z) =
    1
    1 + e z
    0.5
    1
    0
    z
    z = ✓ · X
    Logistic function (also sigmoid function). Asymptotes at 0 and 1. Crosses 0.5 at origin.
    z is just our old dot product, the linear predictor.
    

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55. h✓(X) =
    1
    1 + e ✓·X
    Probability that y=1 for input X
    LOGISTIC REGRESSION
    If hypothesis describes spam, then given X=body of email, if y=0.7, means there’s 70%
    chance it’s spam. Thresholding on that is up to you.
    

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56. Building the Tool
    

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57. Corpus
    collection of source data used for training and
    testing the model
    

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58. Twitter MongoDB
    phirehose
    hooks into streaming API
    

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59. Twitter MongoDB
    phirehose
    8500 tweets
    hooks into streaming API
    

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60. Feature
    Identification
    

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61. independent
    &
    discriminant
    Independent: feature A should not co-occur (correlate) with feature B highly.
    Discriminant: a feature should provide uniquely classifiable data (what letter a tweet starts
    with is not a good feature).
    

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62. ‣
    @a at the end of the tweet
    ‣
    @a...
    ‣
    length < N chars
    ‣
    # of user mentions in the tweet
    ‣
    # of hashtags
    ‣
    language!
    ‣
    @a followed by punctuation and a word
    character (except for apostrophe)
    possible features
    

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63. feature = extractor(tweet)
    For each feature, write a small function that takes a tweet and returns a numeric value
    

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64. corpus
    extractors
    feature vectors
    Run the set of these functions over the corpus and build up feature vectors
    Array of arrays
    Save to DB
    

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65. Language
    Matters
    high correlation between the language of the tweet and its category (good/bad)
    

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66. Indonesian or Tagalog?
    Garbage.
    

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67. id Indonesian 3548
    en English 1804
    tl Tagalog 733
    es Spanish 329
    so Somalian 305
    ja Japanese 300
    pt Portuguese 262
    ar Arabic 256
    nl Dutch 150
    it Italian 137
    sw Swahili 118
    fr French 92
    Top 12 Languages
    I guarantee you people aren’t tweeting at me in Swahili.
    

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68. Language
    Detection
    Can’t trust the language field in user’s profile data.
    Used character N-grams and character sets for detection.
    Has its own error rate, so needs some post-processing.
    

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69. Language
    Detection
    Text_LanguageDetect
    textcat
    pecl /
    pear /
    Can’t trust the language field in user’s profile data.
    Used character N-grams and character sets for detection.
    Has its own error rate, so needs some post-processing.
    

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70. ✓ Clean-up text (remove mentions, links, etc)
    ✓ Run language detection
    ✓ If unknown/low weight, pretend it’s English, else:
    ✓ If not a character set-determined language, try harder:
    ✓ Tokenize into words
    ✓ Difference with English vocabulary
    ✓ If words remain, run parts-of-speech tagger on each
    ✓ For NNS, VBZ, and VBD run stemming algorithm
    ✓ If result is in English vocabulary, remove from remaining
    ✓ If remaining list is not empty, calculate:
    unusual_word_ratio = size(remaining)/size(words)
    ✓ If ratio < 20%, pretend it’s English
    EnglishNotEnglish
    A lot of this is heuristic-based, after some trial-and-error.
    Seems to help with my corpus.
    

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71. BINARY CLASSIFICATION
    Grunt work
    Built a web-based tool to display tweets a page at a time and select good ones
    

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72. feature vectors
    labels (good/bad)
    I N P U T
    O U T P U T
    Had my input and output
    

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73. BIAS
    CORRECTION
    One more thing to address
    

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74. BIAS
    CORRECTION
    BAD GOOD
    99% = bad (less < 100 tweets were good)
    Training a model as-is would not produce good results
    Need to adjust the bias
    

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75. BIAS
    CORRECTION
    BAD GOOD
    

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76. O V E R
    SAMPLING
    Oversampling: use multiple copies of good tweets to equalize with bad
    Problem: bias very high, each good tweet would have to be copied 100 times, and not
    contribute any variance to the good category
    

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77. O V E R
    SAMPLING
    Oversampling: use multiple copies of good tweets to equalize with bad
    Problem: bias very high, each good tweet would have to be copied 100 times, and not
    contribute any variance to the good category
    

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78. O V E R
    SAMPLING
    UNDER
    Undersampling: drop most of the bad tweets to equalize with good
    Problem: total corpus ends up being < 200 tweets, not enough for training
    

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79. SAMPLING
    UNDER
    Undersampling: drop most of the bad tweets to equalize with good
    Problem: total corpus ends up being < 200 tweets, not enough for training
    

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80. OVERSAMPLING
    Synthetic
    Synthesize feature vectors by determining what constitutes a good tweet and do weighted
    random selection of feature values.
    

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81. chance feature
    90% “good” language
    70%
    25%
    5%
    no hashtags
    1 hashtag
    2 hashtags
    2% @a at the end
    85% rand length > 10
    The actual synthesis is somewhat more complex and was also trial-and-error based
    Synthesized tweets + existing good tweets = 2/3 of # of bad tweets in the training corpus
    (limited to 1000)
    

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82. chance feature
    90% “good” language
    70%
    25%
    5%
    no hashtags
    1 hashtag
    2 hashtags
    2% @a at the end
    85% rand length > 10
    1
    The actual synthesis is somewhat more complex and was also trial-and-error based
    Synthesized tweets + existing good tweets = 2/3 of # of bad tweets in the training corpus
    (limited to 1000)
    

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83. chance feature
    90% “good” language
    70%
    25%
    5%
    no hashtags
    1 hashtag
    2 hashtags
    2% @a at the end
    85% rand length > 10
    1
    2
    The actual synthesis is somewhat more complex and was also trial-and-error based
    Synthesized tweets + existing good tweets = 2/3 of # of bad tweets in the training corpus
    (limited to 1000)
    

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84. chance feature
    90% “good” language
    70%
    25%
    5%
    no hashtags
    1 hashtag
    2 hashtags
    2% @a at the end
    85% rand length > 10
    1
    2
    0
    The actual synthesis is somewhat more complex and was also trial-and-error based
    Synthesized tweets + existing good tweets = 2/3 of # of bad tweets in the training corpus
    (limited to 1000)
    

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85. chance feature
    90% “good” language
    70%
    25%
    5%
    no hashtags
    1 hashtag
    2 hashtags
    2% @a at the end
    85% rand length > 10
    1
    2
    0
    77
    The actual synthesis is somewhat more complex and was also trial-and-error based
    Synthesized tweets + existing good tweets = 2/3 of # of bad tweets in the training corpus
    (limited to 1000)
    

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86. Model
    Training
    We have the hypothesis (decision function) and the training set,
    How do we actually determine the weights/parameters?
    

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87. COST
    FUNCTION
    Measures how far the prediction of the system is from the reality.
    The cost depends on the parameters.
    The less the cost, the closer we’re to the ideal parameters for the model.
    

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88. REALITY
    PREDICTION
    COST
    FUNCTION
    Measures how far the prediction of the system is from the reality.
    The cost depends on the parameters.
    The less the cost, the closer we’re to the ideal parameters for the model.
    

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89. COST
    FUNCTION
    J
    (
    ✓
    ) =
    1
    m
    m
    X
    i=1
    Cost
    (
    h✓(
    x
    )
    , y
    )
    Measures how far the prediction of the system is from the reality.
    The cost depends on the parameters.
    The less the cost, the closer we’re to the ideal parameters for the model.
    

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90. Cost(h✓(x), y) =
    (
    log (h✓(x)) if y = 1
    log (1 h✓(x)) if y = 0
    LOGISTIC COST
    

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91. 1
    0
    y=1 y=0
    1
    0
    Correct guess
    Incorrect guess
    Cost = 0
    Cost = huge
    When y=1 and h(x) is 1 (good guess), cost is 0, but the closer h(x) gets to 0 (wrong guess),
    the more we penalize the algorithm. Same for y=0.
    

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92. minimize cost
    OVER θ
    Finding the best values of Theta that minimize the cost
    

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93. GRADIENT DESCENT
    Random starting point.
    Pretend you’re standing on a hill. Find the direction of the steepest descent and take a step.
    Repeat.
    Imagine a ball rolling down from a hill.
    

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94. ✓i
    ↵
    = ✓i
    @J(✓)
    @✓i
    GRADIENT DESCENT
    Each step adjusts the parameters according to the slope
    

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95. ↵
    =
    each parameter
    ✓i ✓i
    @J(✓)
    @✓i
    Have to update them simultaneously (the whole vector at a time).
    

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96. ✓i
    = ✓i
    learning rate
    ↵
    @J(✓)
    @✓i
    Controls how big a step you take
    If α is big have an aggressive gradient descent
    If α is small take tiny steps
    If too small, tiny steps, takes too long
    If too big, can overshoot the minimum and fail to converge
    

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97. ✓i
    ↵
    = ✓i
    derivative
    aka
    “the slope”
    @J(✓)
    @✓i
    The slope indicates the steepness of the descent step for each weight, i.e. direction.
    Keep going for a number of iterations or until cost is below a threshold (convergence).
    Graph the cost function versus # of iterations and see where it starts to approach 0, past that
    are diminishing returns.
    

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98. ✓i =
    ✓i ↵
    m
    X
    j=1
    (
    h✓(
    x
    j)
    y
    j)
    x
    j
    i
    FINAL UPDATE ALGORITHM
    Derivative for logistic regression simplifies to this term.
    Have to update the weights simultaneously!
    

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99. X1 = [1 12.0]
    X2 = [1 -3.5]
    Hypothesis for each data point based on current parameters.
    Each parameter is updated in order and result is saved to a temporary.
    

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100. y1 = 1
     y2 = 0
     X1 = [1 12.0]
     X2 = [1 -3.5]
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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101. y1 = 1
     y2 = 0
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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102. y1 = 1
     y2 = 0
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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103. y1 = 1
     y2 = 0
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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104. y1 = 1
     y2 = 0
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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105. y1 = 1
     y2 = 0
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     T0
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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106. y1 = 1
     y2 = 0
     = 0.1 - 0.05 • ((h(X1) - y1) • X10 + (h(X2) - y2) • X20)
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     T0
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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107. y1 = 1
     y2 = 0
     = 0.1 - 0.05 • ((h(X1) - y1) • X10 + (h(X2) - y2) • X20)
     = 0.1 - 0.05 • ((0.786 - 1) • 1 + (0.438 - 0) • 1)
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     T0
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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108. y1 = 1
     y2 = 0
     = 0.1 - 0.05 • ((h(X1) - y1) • X10 + (h(X2) - y2) • X20)
     = 0.1 - 0.05 • ((0.786 - 1) • 1 + (0.438 - 0) • 1)
     = 0.088
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     T0
     ↵= 0.05
     Hypothesis for each data point based on current parameters.
     Each parameter is updated in order and result is saved to a temporary.
     

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109. y1 = 1
     y2 = 0
     = 0.1 - 0.05 • ((h(X1) - y1) • X10 + (h(X2) - y2) • X20)
     = 0.1 - 0.05 • ((0.786 - 1) • 12.0 + (0.438 - 0) • -3.5)
     = 0.305
     h(X1) = 1 / (1 + e-(0.1 • 1 + 0.1 • 12.0)) = 0.786
     h(X2) = 1 / (1 + e-(0.1 • 1 + 0.1 • -3.5)) = 0.438
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.1 0.1]
     T1
     ↵= 0.05
     Note that the hypotheses don’t change within the iteration.
     

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110. y1 = 1
     y2 = 0
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [T0 T1]
     ↵= 0.05
     Replace parameter (weights) vector with the temporaries.
     

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111. y1 = 1
     y2 = 0
     X1 = [1 12.0]
     X2 = [1 -3.5]
     θ = [0.088 0.305]
     ↵= 0.05
     Do next iteration
     

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112. Trai ning
     CROSS
     Used to assess the results of the training.
     

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113. DATA
     

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114. DATA
     TRAINING
     

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115. DATA
     TRAINING
     TEST
     Train model on training set, then test results on test set.
     Rinse, lather, repeat feature selection/synthesis/training until results are "good enough".
     Pick the best parameters and save them (DB, other).
     

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116. Putting It All
     Together
     Let’s put our model to use, finally.
     The tool hooks into Twitter Streaming API, and naturally that comes with need to do certain
     error handling, etc. Once we get the actual tweet though..
     

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117. Load the model
     The weights we have calculated via training
     Easiest is to load them from DB (can be used to test different models).
     

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118. HARD
     CODED
     RULES
     We apply some hardcoded rules to filter out the tweets we are certain are good or bad.
     The truncate RT ones don’t show up on the Web or other tools anyway, so fine to skip those.
     

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119. HARD
     CODED
     RULES
     SKIP
     truncated retweets: "RT @A ..."
     We apply some hardcoded rules to filter out the tweets we are certain are good or bad.
     The truncate RT ones don’t show up on the Web or other tools anyway, so fine to skip those.
     

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120. HARD
     CODED
     RULES
     SKIP
     truncated retweets: "RT @A ..."
     @ mentions of friends
     We apply some hardcoded rules to filter out the tweets we are certain are good or bad.
     The truncate RT ones don’t show up on the Web or other tools anyway, so fine to skip those.
     

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121. HARD
     CODED
     RULES
     SKIP
     truncated retweets: "RT @A ..."
     tweets from friends
     @ mentions of friends
     We apply some hardcoded rules to filter out the tweets we are certain are good or bad.
     The truncate RT ones don’t show up on the Web or other tools anyway, so fine to skip those.
     

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122. Classifying Tweets
     This is the moment we’ve been waiting for.
     

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123. Classifying Tweets
     GOOD
     This is the moment we’ve been waiting for.
     

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124. Classifying Tweets
     GOOD BAD
     This is the moment we’ve been waiting for.
     

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125. h✓(X) =
     1
     1 + e ✓·X
     Remember this?
     First is our hypothesis.
     

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126. h✓(X) =
     1
     1 + e ✓·X
     Remember this?
     ✓·X = ✓0 + ✓1X1 + ✓2X2 + . . .
     First is our hypothesis.
     

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127. Finally
     h✓(X) =
     1
     1 + e (✓0+✓1X1+✓2X2+... )
     If h > 0.5 , tweet is bad, otherwise good
     Remember that the output of h() is 0..1 (probability).
     0.5 is your threshold, adjust it for your degree of tolerance. I used 0.9 to reduce false
     positives.
     

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128. extract features
     3 simple steps
     Invoke the feature extractor to construct the feature vector for this tweet.
     Evaluate the decision function over the feature vector (input the calculated feature
     parameters into the equation).
     Use the output of the classifier.
     

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129. extract features
     run the model
     3 simple steps
     Invoke the feature extractor to construct the feature vector for this tweet.
     Evaluate the decision function over the feature vector (input the calculated feature
     parameters into the equation).
     Use the output of the classifier.
     

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130. extract features
     run the model
     act on the result
     3 simple steps
     Invoke the feature extractor to construct the feature vector for this tweet.
     Evaluate the decision function over the feature vector (input the calculated feature
     parameters into the equation).
     Use the output of the classifier.
     

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131. BAD?
     block
     user!
     Also save the tweet to DB for future analysis.
     

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132. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     

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133. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     -Connection handling, backoff in case of problems, undocumented API errors, etc.
     

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134. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     -No way for blocked person to get ahold of you via Twitter anymore, so when training the
     model, err on the side of caution.
     

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135. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     -Some tweets are shown on the website, but never seen through the API.
     

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136. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     -Lots of room for improvement.
     

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137. Lessons Learned
     Blocking is the only option (and is final)
     Streaming API delivery is incomplete
     ReplyCleaner judged to be ~80% effective
     Twitter API is a pain in the rear
     PHP sucks at math-y stuff
     -Lots of room for improvement.
     

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138. NEXT
     STEPS
     ★ Realtime feedback
     ★ More features
     ★ Grammar analysis
     ★ Support Vector Machines or
     decision trees
     ★ Clockwork Raven for manual
     classification
     ★ Other minimization algos:
     BFGS, conjugate gradient
     ★ Need pecl/scikit-learn
     Click on the tweets that are bad and it immediately incorporates them into the model.
     Grammar analysis to eliminate the common "@a bar" or "two @a time" occurrences.
     SVMs more appropriate for biased data sets.
     Farm out manual classification to Mechanical Turk.
     May help avoid local minima, no need to pick alpha, often faster than GD.
     

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139. TOOLS
     ★ MongoDB
     ★ pear/Text_LanguageDetect
     ★ English vocabulary corpus
     ★ Parts-Of-Speech tagging
     ★ SplFixedArray
     ★ phirehose
     ★ Python’s scikit-learn (for
     validation)
     ★ Code sample
     MongoDB (great fit for JSON data)
     English vocabulary corpus: http://corpus.byu.edu/ or http://www.keithv.com/software/wlist/
     SplFixedArray in PHP (memory savings and slightly faster)
     

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140. Questions?
     

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