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Introduction to Text Mining and Semantics

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Introduction to Text Mining and Semantics, presented by Seth Grimes at Nstein seminars in London, June 2009, and New York, September 2009.

Introduction to Text Mining and Semantics, presented by Seth Grimes at Nstein seminars in London, June 2009, and New York, September 2009.

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  • 1. Introduction to Text Miningand Semantics
    Seth Grimes
    --
    President, Alta Plana
  • 2. New York Times
    October 9, 1958
  • 3. From text to information
    “Text expresses a vast, rich range of information, but encodes this information in a form that is difficult to decipher automatically.”
    -- Marti A. Hearst,
    “Untangling Text Data Mining,” 1999
  • 4. Document input and processing
    Information extraction
    Knowledge management
    Hans Peter Luhn, “A Business Intelligence System,” IBM Journal, October 1958
  • 5. Statistical analysis of content
    “Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance.”
    Hans Peter Luhn,
    “The Automatic Creation of Literature Abstracts,”
    IBM Journal, April 1958
  • 6. Statistical analysis limitations
    “This rather unsophisticated argument on ‘significance’ avoids such linguistic implications as grammar and syntax... No attention is paid to the logical and semantic relationships the author has established.”
    -- Hans Peter Luhn, 1958
  • 7. Semantic links
    New York Times,
    September 8, 1957
    Anaphora / coreference: “They”
  • 8. Digital content universe
    “The broadcast, media, and entertainment industries garner about 4% of the world’s revenues but already generate, manage, or otherwise oversee 50% of the digital universe.”
    “The Diverse and Exploding Digital Universe,”
    (IDC, 2008)
    Approximately 70% of the digital universe is created by individuals.
  • 9. The “unstructured data” challenge
    The digital universe:
    • Web sites, news & journal articles, images, video.
    • 10. Blogs, forum postings, and social media.
    • 11. E-mail, Contact-center notes and transcripts; recorded conversation.
    • 12. Surveys, feedback forms, warranty & insurance claims.
    • 13. Office documents, regulatory filings, reports, scientific papers.
    • 14. And every other sort of document imaginable.
    Is Search up to the job?
  • 15. Search results
    How are the quality, value & authority of search results?
    Hotel’s opinion
    Who profits from search?
    Guest’s opinion - about Priceline
  • 16. From Web 1.0 to Web 2.0
    How can we do better?
    “We have many of the tools in place -- from Web 2.0 technologies…”
    “The Diverse and Exploding Digital Universe,”
    (IDC, 2008)
  • 17. Web 2.0
    “Web 2.0 is the business revolution in the computer industry caused by the move to the Internet as a platform.” -- Tim O’Reilly, 2004
    “[A] move from personal websites to blogs and blog site aggregation, from publishing to participation,… an ongoing and interactive process... to links based on tagging.”
    -- Terry Flew, “New Media: An Introduction,” 2008
    Web 2.0 is dynamic, personalized, interactive, collaborative.
  • 18. From information growth to economicgrowth
    “We have many of the tools in place -- from Web 2.0 technologies… to unstructured data search software and the Semantic Web -- to tame the digital universe. Done right, we can turn information growth into economic growth.”
    -- “The Diverse and Exploding Digital Universe,” (IDC, 2008)
  • 19. Text mining: from information to intelligence
    Text mining enables smarter search that better responds to user goals, e.g., answers –
  • 20. From Web 2.0 to Web 3.0
    For even better findability:
    “The Semantic Web is a web of data, in some ways like a global database.”
    -- Tim Berners-Lee, 1998
    Web 3.0 is Web 2.0 + the Semantic Web + semantic tools.
    Recurring themes:
    • Semantically enriched content & search.
    • 21. Linked Data.
    • 22. Context sensitive.
    • 23. Location aware.
  • The Semantic Web vision
    "
    An open-standards architure, coordinated by the W3C (World Wide Web Consortium)
    Linked Data: “exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web.”
  • 24. Steps in the right direction…
  • 25. … and missteps
    “Kind” = type, variety, not a sentiment.
    Complete misclassification
    External reference
    Unfiltered duplicates
  • 26. Getting to Web 3.0
    Text mining / analytics enables Web 3.0 and the Semantic Web.
    • Automated content categorization and classification.
    • 27. Text augmentation: metadata generation, content tagging.
    • 28. Information extraction to databases.
    • 29. Exploratory analysis and visualization.
    Technical concepts:
    • Linked Data
    • 30. RDF, SPARQL, OWL
    • 31. RDFa, Microformats, eRDF
  • Text mining: users’ perspective
    I recently published a study report, “Text Analytics 2009: User Perspectives on Solutions and Providers.”
    I estimated a $350 million global market in 2008, up 40% from 2007.
    I relayed findings from a survey that asked…
  • 32. Primary applications
    What are your primary applications where text comes into play?
  • 33. Analyzedtextual information
    What textual information are you analyzing or do you plan to analyze?
    Current users responded:
  • 34. Extracted information
    Do you need (or expect to need) to extract or analyze:
  • 35. Overall satisfaction
    Please rate your overall experience – your satisfaction – with text mining.
  • 36. Movingahead
    Apply text mining to discover value in content.
    Develop / improve metadata and taxonomies.
    Adopt semantic technologies -- for content publishing and for user interactions -- to boost flexibility, findability, and profitability.
    And understand your audience:
    “By focusing on the fundamental aspects of the consumers’ online behavior -- not just current best practices -- companies will be better prepared when Web 2.0+ morphs into Web 3.0 and beyond.”
    -- Donna L. Hoffman, UC Riverside, in the McKinsey Quarterly