Global Analytics: Text, Speech, Sentiment, and Sense
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Global Analytics: Text, Speech, Sentiment, and Sense
- 1. Global Analytics: Text, Speech, Sentiment, and Sense Seth Grimes Alta Plana Corporation @sethgrimes December 4, 2014
- 2. Global Analytics: Text, Speech, Sentiment, and Sense 2 “Reading from Text is a Hard Problem” Thus the Orb he roam'd With narrow search; and with inspection deep Consider'd every Creature, which of all Most opportune might serve his Wiles. -- John Milton, Paradise Lost LT-Accelerate – 4 December, 2014 Eugène Delacroix, St. Michael Defeats the Devil
- 3. Global Analytics: Text, Speech, Sentiment, and Sense 3 “Reading from Text is a Hard Problem” Thus the Orb he roam'd With narrow search; and with inspection deep Consider'd every Creature, which of all Most opportune might serve his Wiles. -- John Milton, Paradise Lost LT-Accelerate – 4 December, 2014 Eugène Delacroix, St. Michael Defeats the Devil Data Space, Indexing Searc h Analysis Intent, Goals Context
- 4. Global Analytics: Text, Speech, Sentiment, and Sense 4 LT-Accelerate – 4 December, 2014
- 5. Global Analytics: Text, Speech, Sentiment, and Sense 5 Analytics is the systematic application of algorithmic methods that derive and deliver information, typically expressed quantitatively, whether in the form of indicators, tables, visualizations, or models. • Systematic means formal & repeatable. • Algorithmic contrasts with heuristic. Analytics creates and/or applies models. LT-Accelerate – 4 December, 2014
- 6. Global Analytics: Text, Speech, Sentiment, and Sense 6 Models make the unstructured computable. LT-Accelerate – 4 December, 2014 http://www.tropicalisland.de/NYC_New_York_ Brooklyn_Bridge_from_World_Trade_Center_ b.jpg x(t) = t y(t) = ½ a (et/a + e-t/a) = acosh(t/a) http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg
- 7. Global Analytics: Text, Speech, Sentiment, and Sense 7 Sixty+ years of analysis & modelling progress: LT-Accelerate – 4 December, 2014 Text Numbers Patterns & Insights Connections Interactions
- 9. Document input and processing Knowledge handling is key Desk Set (1957): Computer engineer Richard Sumner (Spencer Tracy) and television network librarian Bunny Watson (Katherine Hepburn) and the "electronic brain" EMERAC. Hans Peter Luhn “A Business Intelligence System” IBM Journal, October 1958
- 10. Global Analytics: Text, Speech, Sentiment, and Sense 10 Luhn’s analysis of Messengers of the Nervous System, a Scientific American article http://wordle.net, applied to a Luhn-cited NY Times article “Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the auto-abstract.” H.P. Luhn, The Automatic Creation of Literature Abstracts, IBM Journal, 1958. LT-Accelerate – 4 December, 2014
- 11. Global Analytics: Text, Speech, Sentiment, and Sense 11 LT-Accelerate – 4 December, 2014 “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.” -- H.P. Luhn ~ 2004-5
- 15. Global Analytics: Text, Speech, Sentiment, and Sense 15 Patterns, Insights & Connections ~ 2009- 12 LT-Accelerate – 4 December, 2014
- 16. Global Analytics: Text, Speech, Sentiment, and Sense 16 LT-Accelerate – 4 December, 2014 … also commonly explored via dashboards.
- 17. Global Analytics: Text, Speech, Sentiment, and Sense 17 Do you currently need (or expect to need) to extract or analyze... Expect, 22% Expect, 28% Expect, 33% Expect, 25% Expect, 23% Expect, 23% Expect, 24% LT-Accelerate – 4 December, 2014 Current, 66% Current, 54% Current, 47% Current, 56% Current, 51% Current, 47% Current, 34% Current, 31% Current, 33% Expect, 21% Expect, 28% Topics and themes Sentiment, opinions, attitudes, emotions,… Relationships and/or facts Named entities – people, companies, … Concepts, that is, abstract groups of entities Metadata such as document author,… Other entities – phone numbers, part/product … 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Semantic annotations Events Text Analytics 2014 http://altaplana.com/TA2014 What information?
- 18. Global Analytics: Text, Speech, Sentiment, and Sense 18 Emotion and outcomes LT-Accelerate – 4 December, 2014
- 19. Global Analytics: Text, Speech, Sentiment, and Sense 19 LT-Accelerate – 4 December, 2014 “The share rise in users who selected Arabic…coincided with much of the civil unrest… in Middle Eastern countries.” http://bits.blogs.nytimes.com/2014/03/09/the -languages-of-twitter-users/
- 20. Global Analytics: Text, Speech, Sentiment, and Sense 20 24% LT-Accelerate – 4 December, 2014 Non-English language support? 5% 2% 1% 3% Other Other European or Slavic/Cyrillic Other East Asian Other Arabic script (including Urdu,… 2% 1% 7% 10% 3% 4% Other African Turkish or Turkic Spanish Scandinavian or Baltic Russian Portuguese Polish Korean Japanese Italian Hindi, Urdu, Bengali, Punjabi, or other… 2% 1% 0% 16% 9% 34% 36% 2% 18% 7% 13% 8% 38% 3% 9% 17% 3% 28% 7% 17% 2% 10% 11% 15% 8% 4% 17% 21% 3% 20% 4% 0% 2% 0% 10% 20% 30% 40% 50% 60% Greek German French Dutch Chinese Bahasa Indonesia or Malay Arabic Current Within 2 years Text Analytics 2014 http://altaplana.com/TA2014
- 21. Global Analytics: Text, Speech, Sentiment, and Sense 21 LT-Accelerate – 4 December, 2014 Audio including speech Images Video IOT http://www.geekosystem.com/ facebook-face-recognition/ http://www.sciencedirect.com/science /article/pii/S0167639312000118 http://flylib.com/books/en/2.495.1.54/1/ Beyond text
- 22. Global Analytics: Text, Speech, Sentiment, and Sense 22 http://searchuserinterfaces.com/ LT-Accelerate – 4 December, 2014 “It is convenient to divide the entire information access process into two main components: information retrieval through searching and browsing, and analysis and synthesis of results. This broader process is often referred to in the literature as sensemaking. Sensemaking refers to an iterative process of formulating a conceptual representation from of a large volume of information.” – Marti Hearst, 2009 Sensemaking
- 23. Global Analytics: Text, Speech, Sentiment, and Sense 23 Challenges Context Interaction Narrative and discourse Correlation, integration, and synthesis Sentiment++: Mood, opinions, emotions, intent Question answering Dialog, storytelling Cross-lingual / “omni-channel” implementation … Prescription, autonomy … Singularity? LT-Accelerate – 4 December, 2014
- 24. Global Analytics: Text, Speech, Sentiment, and Sense 24 Opportunity enablers LT-Accelerate – 4 December, 2014 The API economy I.e., on-demand, via-API Web services Cloud deployment and service delivery …enabling rapid deployment Data aggregation and enrichment Examples: Gnip, DataSift, Spinn3r, and Moreover Growth hacking Knowledge graphs Machine learning Supervised, unsupervised, active, deep Open source Platforms and frameworks Examples: UIMA, GATE… Salesforce, QlikView… Python, R
- 25. Global Analytics: Text, Speech, Sentiment, and Sense 25 Where to? LT-Accelerate – 4 December, 2014
- 26. Global Analytics: Text, Speech, Sentiment, and Sense Seth Grimes Alta Plana Corporation @sethgrimes December 4, 2014