Practical Machine Learning and Rails Part2

SENTIMENTCLASSIFICATION

TRAINING DATA:

TRAINING DATA:- tweets

TRAINING DATA:- tweets- positive/negative

TRAINING DATA:- tweets- positive/negative - use emoticons from twitter

TRAINING DATA:- tweets- positive/negative - use emoticons from twitter :-) or :-(

BUILDING TRAINING DATA: NEGATIVE is upset that he cant update his Facebook by texting it... and might cry as a result School today also. Blah! I couldnt bear to watch it. And I thought the UA loss was embarrassing I hate when I have to call and wake people up POSITIVE Just woke up. Having no school is the best feeling ever Im enjoying a beautiful morning here in Phoenix dropping molly off getting ice cream with Aaron

FEATURES:

FEATURES: BAG OF WORDS MODEL

FEATURES: BAG OF WORDS MODEL split the text into words, create a dictionary, and replace text with word counts

BAG OF WORDS

BAG OF WORDStweets:I ran fastBob ran farI ran to Bob

BAG OF WORDStweets:I ran fastBob ran farI ran to Bob dictionary = %w{I ran fast Bob far to}

BAG OF WORDStweets: word vectors:I ran fast [1 1 1 0 0 0]Bob ran far [0 1 0 1 1 0]I ran to Bob [1 1 0 1 0 1] dictionary = %w{I ran fast Bob far to}

CLASSIFIER:

CLASSIFIER: training examples:word vector -> labels

CLASSIFIER: training examples: word vector -> labelsclassiﬁcation algorithm

CLASSIFIER: training examples: word vector -> labelsclassiﬁcation algorithm model

WEKA• open source java app

WEKA• open source java app• contains common ML algorithms

WEKA• open source java app• contains common ML algorithms• gui interface

WEKA• open source java app• contains common ML algorithms• gui interface• can access it from jruby

WEKA• open source java app• contains common ML algorithms• gui interface• can access it from jruby• helps with:

WEKA• open source java app• contains common ML algorithms• gui interface• can access it from jruby• helps with: • converting words into vectors

WEKA• open source java app• contains common ML algorithms• gui interface• can access it from jruby• helps with: • converting words into vectors • training/test, cross-validation, metrics

ARFF FILE

TRAINING IN WEKA[SHOW EXAMPLE HERE]

EVALUATION• correctly classiﬁed• mean squared error

EVALUATIONfalse negative/positives

SENTIMENT CLASSIFICATION EXAMPLEhttps://github.com/ryanstout/mlexample

QUERYINGarff_path = Rails.root.join("data/sentiment.arff").to_sarff = FileReader.new(arff_path)model_path = Rails.root.join("models/sentiment.model").to_sclassifier = SerializationHelper.read(model_path)data = begin Instances.new(arff,1).tap do |instance| if instance.class_index == -1 instance.set_class_index(instance.num_attributes - 1) end endend

QUERYINGinstance = SparseInstance.new(data.num_attributes)instance.set_dataset(data)instance.set_value(data.attribute(0), params[:sentiment][:message])result = classifier.distribution_for_instance(instance).firstpercent_positive = 1 - result.to_f@message = "The text is #{(percent_positive*100.0).round}% positive"

HOW DO WE IMPROVE?

HOW DO WE IMPROVE?•bigger dictionary

HOW DO WE IMPROVE?•bigger dictionary•bi-grams/tri-grams

HOW DO WE IMPROVE?•bigger dictionary•bi-grams/tri-grams•part of speech tagging

HOW DO WE IMPROVE?•bigger dictionary•bi-grams/tri-grams•part of speech tagging•more data

Feature Generation

Feature Generation think about what information is valuable to an expert

Feature Generation think about what information is valuable to an expert remove data that isnt useful (attribute selection)

ATTRIBUTE SELECTION[SHOW ATTRIBUTE SELECTIONEXAMPLE]

ATTRIBUTESELECTION

DOMAIN PRICE PREDICTION• predict how much a domain would sell for

TRAINING DATA

TRAINING DATA• domains

TRAINING DATA• domains• historical sale prices for domains

FEATURES

FEATURES• split domain by words

FEATURES• split domain by words• generate features for each word

FEATURES• split domain by words• generate features for each word • how common the word is

FEATURES• split domain by words• generate features for each word • how common the word is • number of google results for each word

FEATURES• split domain by words• generate features for each word • how common the word is • number of google results for each word • cpc for the word

ALGORITHMsupport vector regression functions > SMOreg in weka

WHAT WE DIDN’T COVER

WHAT WE DIDN’T COVER• collaborative ﬁltering

WHAT WE DIDN’T COVER• collaborative ﬁltering• clustering

WHAT WE DIDN’T COVER• collaborative ﬁltering• clustering• theorem proving (classical AI)

ADDITIONAL RESOURCESstanford machine learning class ml-class.org

TOOLS• weka• libsvm, liblinear• vowpal wabbit (big dictionaries)• recommendify • https://github.com/paulasmuth/recommendify

QUESTIONScontact us on twitter at@tectonic and @ryanstout

http://blog.andrewcantino.com	1092
http://lanyrd.com	218
http://feeds.feedburner.com	28
http://localhost	25
http://localhost:4000	3
https://si0.twimg.com	1
http://www.hanrss.com	1
https://twimg0-a.akamaihd.net	1
http://translate.googleusercontent.com	1

Practical Machine Learning and Rails Part2

by ryanstout

Accessibility

Categories

Upload Details

Usage Rights

9 Embeds 1,370

Statistics

Practical Machine Learning and Rails Part2 Presentation Transcript