Machine learning applications on text data
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o you get the feeling of ‘the cart before the horse’ on hearing buzz-words like social data mining or sentiment analysis and so on? Fundamental text mining methods are the real ‘workhorses’ behind these buzz-words. This prsentation aims to give understanding of the fundamentals in plain english.
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Machine learning applications on text data
- 1. Using Machine learning and R Finding Order in the Chaos Harshad Saykhedkar
- 2. The main ideaSource of text and applications Emails Spam detection Product descriptions / reviews Sentiment analysis, recommendation Blogs / informational content Content recommendations Web pages / news articles Topic identification, trending topics Tweets / comments / social content Sentiment analysis, named entity recognition
- 3. (Text mining) is a wonderful world. Let's go exploring...! The main ideaThe main idea
- 4. Itinerary ● R you ready ? ● Prep camp ● The wandering traveller ● The seeker
- 5. R you ready ?
- 6. The main ideaPacking our bags : Checks ● Starting R ● Loading required packages ● Check sessionInfo( )
- 7. The main ideaPacking our bags : Datatypes Atomic Vector Lists "Let's try our hands"
- 8. The main ideaPacking our bags : Functions ● Expressions which are evaluated ● Can be passed around ● Definitions can be nested Details not covered : Argument matching, Call by value, Environments and lexical scoping, Promises etc..
- 9. Prep Camp
- 10. The main ideaPrep camp : Sentiment Analysis ● Bag of words model ● Simple aggregated score ' terrible service & disorganised ' ' OK - some good some bad ' ' Great location, fabulous staff '
- 11. The main idea ● Part of speech ambiguity ● Further exploration ? ● Equal weightage model ● Double negations ? Prep camp : Improvements
- 13. The main ideawandering traveller : Unsupervised Learning Can define distance Entity as point in space How to derive this model for text ? Feature 1 Feature 2
- 14. The main ideawandering traveller : Vector Space Model Word, Phrase, Theme Comments, Blogs, Tweets Word, Phrase, Theme
- 15. The main ideawandering traveller : TfIdf and other details " But how to measure the importance of a word for a doc ? " ● Binary : Is the 'word' in the 'doc' ? ● Tf : # times the word in the 'doc' ? ● TfIdf : Penalize the obvious!
- 16. The main ideawandering traveller : Hierarchical Clustering ● Define distance measure ● Keep Merging based on similarity Washing Machine Washer Dryer Camera
- 17. The main ideawandering traveller : Improvements ● Stemming, lemmatization ● Latent semantic analysis "Cameras" Vs "Camera" "Phone" "Touch Screen"
- 18. The Seeker
- 19. The main ideaSeeker : Supervised Learning ● Labels given with features ● Find rule, classify unobserved case Feature 1 Feature 2
- 20. The main ideaSeeker : Naive Bayes Classifier ● Independence of features ● Train the model on training set ● Test accuracy on a holdout sample Predicted 0 Predicted 1 Actual 0 F (0, 0) F(0, 1) Actual 1 F (1, 0) F(1, 1)
- 21. Learnings
- 22. The main ideaLearnings ● How to cleanup and preprocess data in text form ? ● How to model the data ? ● How to cluster the data ? ● How to classify the data ?
- 23. The main ideaSource of text and applications Emails Spam detection Product descriptions / reviews Sentiment analysis, recommendation Blogs / informational content Content recommendations Web pages / news articles Topic identification, trending topics Tweets / comments / social content Sentiment analysis, named entity recognition
- 24. Questions ?
- 25. "Avid R learner, trying to apply bunch of these techniques to the digital ads world" Contact harshad.saykhedkar@sokrati.com The main ideaAbout me