1. 50 ways to make
computers
understand
natural language

2. with 6 key
techniques

3. Classifiers
Bayesian Classifiers
Support Vector Machine

4. Sentence Parsing
& Tagging
Treat gem
Ruby Linguistics
Open Calais
Achemy API

5. Dictionaries
Ruby WordNet
Create your own

6. Pattern Matching
Regular Expressions (also great for
normalising)

7. Meta Data
HTML, headers, microformats,
author information

8. Statistical
Analysis
Keyword density, word
distribution, word/time
distribution

9. 1
Person Names
Semantic Tagging/Dictionary

10. 2
Location Names
Semantic Tagging + Geocode with
Yahoo API/Google Maps

11. 3
Important Words
Dictionary, Meta Data

12. 4
Email Address
Pattern matching
/b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,4}b/

13. 5
Phone Numbers
Pattern matching
/b[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,4}b/

14. 6
Postal Address
Pattern matching
detect postal code or town/city and work backwards

15. 7
Dates & Times
Dictionary, Pattern matching,
Chronic Gem

16. 8
Company/Org
Semantic Tagging

17. 9
Natural
Phenomenon
Semantic Tagging, Dictionary

18. 10
Job Title
Dictionary

19. 11
Topic
Classifier/Dictionary

20. 12
Quotes
Pattern Matching & Semantic
Tagging

21. 13
Currency
Pattern Matching

22. 14
Language
Classifier

23. 15
Sentiment
Classifier/Dictionary/Pattern
Matching

24. 16
Potty Mouth/
DHH Detector
F-bomb dictionary

25. 17
Flamboyance
Dictionary or Classifier

26. 18
Age
Classifier

27. 19
Wealth
Dictionary/Classifier/Meta data

28. 20
Humour
Dictionary

29. 21
Gender or
Dictionary (first names)
Classifier (guess from writing
style)

30. 22
Author Location
Meta Data

31. 23
Person Details
Meta Data: Wikipedia, Rapleaf

32. 24
Keyword Density
Frequency analysis

33. 25
Relationship
GraphsMeta
Semantic Tagging,
Data(Showing connections
between people mentioning each
other)

34. 26
Clustering
k-means clustering, hierarchical,
clustering, density clustering,
rsruby

35. 27
Influencial
People
Semantic Tagging, Statistical
Analysis

36. 28
Influential Links
Pattern Matching, Statistical
Analysis

37. 29
Social Graph
Meta Data

38. 30
Link Thumbnails
Pattern Matching, Meta data

39. 31
Link Media Type
Pattern Matching, Meta Data or
Dictionary

40. 32
Domain Details
Meta Data (whois, alexa, ip,
geocode)

41. 33
Active Time
Meta Data, Statistical Analysis

42. 34
Generosity
(Through citation e.g. RT) Pattern
Match, Semantic Tagging

43. 35
Similarity
Statistical Analysis: cosine
similarity, vector space model

44. 36
Ethnicity
Classifier

45. 37
Expertise Level
influence + topic + dictionary

46. 38
Tech Savviness
previous or Classifier

47. 39
Device
Ownership
Meta data, dictionary

48. 40
Favourite Brands
Semantic Tagging or Dictionary +
Sentiment Analysis + Frequency
Analysis

49. 41
Favourite
Shopping times
Semantic Tagging or Dictionary +
Sentiment Analysis + Frequency
Analysis

50. 42
Favourite website
Pattern matching + Sentiment
Analysis + Frequency Analysis

51. 43
Organisation or
Person
Meta data or classifier

52. 44
Happiest Places
Meta data, geocoding, sentiment,
statistical analysis

53. 45
Happiest Times
Meta data, sentiment, statistical
analysis

54. 46
Personality Type
Classifier

55. 47
Humanality
Classifier

56. 48
Spam Detection
Classifier

57. 49
Product/Brand/
Film league
dictionary,
sentiment analysis, statistical
analysis

58. 50
Formality
Classifier/Dictionary

59. ?