Text Analytics Past, Present & Future: An Industry View

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Keynote presentation to JADT.org, June 5, 2014

Keynote presentation to JADT.org, June 5, 2014

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  • 1. Text Analytics Past, Present & Future: An Industry View Seth Grimes Alta Plana Corporation @sethgrimes June 5, 2014
  • 2. Text Analytics: An Industry View JADT – June 5, 2014 2
  • 3. Text Analytics: An Industry View JADT – June 5, 2014 3 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.
  • 4. Text Analytics: An Industry View JADT – June 5, 2014 4 Text analytics past: Pioneers…
  • 5. 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
  • 6. Text Analytics: An Industry View JADT – June 5, 2014 6 “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.
  • 7. Text Analytics: An Industry View JADT – June 5, 2014 10 Pipelines and patterns IBM’s MedTAKMI, 1997- http://www.research.ibm.com/trl/projects/textmining/index_e.htm
  • 8. Text Analytics: An Industry View JADT – June 5, 2014 11 Exhaustive extraction An (old) Attensity example – NLP to identify roles and relationships, for a law-enforcement application .
  • 9. Text Analytics: An Industry View JADT – June 5, 2014 12 Language engineering GATE: General Architecture for Text Engineering. http://gate.ac.uk/
  • 10. Text Analytics: An Industry View JADT – June 5, 2014 13 Text analytics present: Business, technology, applications, and solutions…
  • 11. Text Analytics: An Industry View JADT – June 5, 2014 14 “Organizations embracing text analytics all report having an epiphany moment when they suddenly knew more than before.” -- Philip Russom, the Data Warehousing Institute, 2007 http://tdwi.org/articles/2007/05/09-what-works/bi-search-and-text-analytics.aspx
  • 12. Text Analytics: An Industry View JADT – June 5, 2014 15 Linguistics, statistics, and semantics Text analytics (typically) involves linguistic modelling, statistical characterization, learned patterns, and semantic understanding of text-derived features – Named entities: people, companies, places, etc. Pattern-based features: e-mail addresses, phone numbers, etc. Concepts: abstractions of entities. Facts and relationships. Events. Concrete and abstract attributes (e.g., “expensive” & “comfortable”) including measure-value pairs. Subjectivity in the forms of opinions, sentiments, and emotions: attitudinal data. – applied to business ends.
  • 13. Text Analytics: An Industry View JADT – June 5, 2014 16 Sources It’s a truism that 80% of enterprise-relevant information originates in “unstructured” form: E-mail and messages. Web pages, online news & blogs, forum postings, and other social media. Contact-center notes and transcripts. Surveys, feedback forms, warranty claims. Scientific literature, books, legal documents. ... Non-text “unstructured” content? Images Audio including speech Video Value derives from patterns.
  • 14. Text Analytics: An Industry View JADT – June 5, 2014 17 Value What do we do with text, whether online, on-social, or in the enterprise? 1. Post/Publish, Manage, and Archive. 2. Index and Search. 3. Categorize and Classify according to metadata & contents. 4. Extract information and Analyze.
  • 15. Text Analytics: An Industry View JADT – June 5, 2014 18 Semantics, analytics, and IR Text analytics generates semantics to bridge search, BI, and applications, enabling next-generation information systems. Search BI/Big Data Applica- tions Search based applications (search + text + apps) Information access (search + analytics) Synthesis (text + BI)/(big data) Text analytics (inner circle) Semantic search (search + text) NextGen CRM, EFM, MR, marketing, apps…
  • 16. Text Analytics: An Industry View JADT – June 5, 2014 19 Content, composites, connections 1
  • 17. Text Analytics: An Industry View JADT – June 5, 2014 20 Content, Composites, Connections, 2 Content, composites, connections 2
  • 18. Text Analytics: An Industry View JADT – June 5, 2014 21 Applications Text analytics has applications in: Intelligence & law enforcement. Life sciences & clinical medicine. Media & publishing including social-media analysis and contextual advertizing. Competitive intelligence. Voice of the Customer: CRM, product management & marketing. Public administration & policy. Legal, tax & regulatory (LTR) including compliance. Recruiting.
  • 19. Text Analytics: An Industry View JADT – June 5, 2014 22 Opinion, sentiment & emotion
  • 20. Text Analytics: An Industry View JADT – June 5, 2014 23 Sentiment analysis A specialization, of relevance to: Brand/reputation management. Customer experience management (CEM). Competitive intelligence. Survey analysis (EFM = Enterprise Feedback Management). Market research. Product design/quality. Trend spotting.
  • 21. Text Analytics: An Industry View JADT – June 5, 2014 24 Data exploration via dashboards and workbenches.
  • 22. Text Analytics: An Industry View JADT – June 5, 2014 25 Text analytics present: The market…
  • 23. Text Analytics: An Industry View JADT – June 5, 2014 26 http://altaplana.com/TA2014
  • 24. Text Analytics: An Industry View JADT – June 5, 2014 27 5% 6% 8% 9% 10% 11% 13% 14% 15% 16% 25% 27% 29% 33% 38% 38% 39% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Military/national security/intelligence Law enforcement Intellectual property/patent analysis Financial services/capital markets Product/service design, quality assurance, or warranty claims Other Insurance, risk management, or fraud E-discovery Life sciences or clinical medicine Online commerce including shopping, price intelligence,… Content management or publishing Customer /CRM Search, information access, or Question Answering Competitive intelligence Brand/product/reputation management Research (not listed) Voice of the Customer / Customer Experience Management What are your primary applications where text comes into play?
  • 25. Text Analytics: An Industry View JADT – June 5, 2014 28 Voice of the Customer Text analytics is applied to improve customer service and boost satisfaction and loyalty. Analyze customer interactions and opinions – • E-mail, contact-center notes, survey responses. • Forum & blog posting and other social media. – to – • Address customer product & service issues. • Improve quality. • Manage brand & reputation. Assessment of qualitative information from text helps users – • Gain feedback on interactions. • Assess customer value. • Understand root causes. • Mine data for measures such as churn likelihood.
  • 26. Text Analytics: An Industry View JADT – June 5, 2014 29 The commercial scene
  • 27. Text Analytics: An Industry View JADT – June 5, 2014 30 Online commerce Text analytics is applied for marketing, search optimization, competitive intelligence. Analyze social media and enterprise feedback to understand the Voice of the Market: • Opportunities • Threats • Trends Categorize product and service offerings for on-site search and faceted navigation and to enrich content delivery. Annotate pages to enhance Web-search findability, ranking. Scrape competitor sites for offers and pricing. Analyze social and news media for competitive information.
  • 28. Text Analytics: An Industry View JADT – June 5, 2014 31 E-Discovery and compliance Text analytics is applied for compliance, fraud and risk, and e-discovery. Regulatory mandates and corporate practices dictate – • Monitoring corporate communications • Managing electronic stored information for production in event of litigation Sources include e-mail (!!), news, social media Risk avoidance and fraud detection are key to effective decision making • Text analytics mines critical data from unstructured sources • Integrated text-transactional analytics provides rich insights
  • 29. Text Analytics: An Industry View JADT – June 5, 2014 32 16% 19% 20% 20% 22% 26% 31% 31% 32% 36% 37% 38% 42% 61% 0% 20% 40% 60% 80% Web-site feedback social media not listed above chat employee surveys contact-center notes or transcripts e-mail and correspondence online reviews scientific or technical literature Facebook postings on-line forums customer/market surveys comments on blogs and articles news articles blogs (long form+micro) What textual information are you analyzing or do you plan to analyze? 2014 2011 2009
  • 30. Text Analytics: An Industry View JADT – June 5, 2014 33 5% 5% 5% 5% 7% 9% 11% 11% 12% 12% 12% 13% 16% 19% 20% 20% 22% 26% 31% 31% 32% 36% 37% 38% 42% 43% 46% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% insurance claims or underwriting notes point-of-service notes or transcripts video or animated images warranty claims/documentation photographs or other graphical images crime, legal, or judicial reports or evidentiary materials field/intelligence reports speech or other audio patent/IP filings other text messages/instant messages/SMS medical records Web-site feedback social media not listed above chat employee surveys contact-center notes or transcripts e-mail and correspondence online reviews scientific or technical literature Facebook postings on-line forums customer/market surveys comments on blogs and articles news articles blogs (long form) including Tumblr Twitter, Sina Weibo, or other microblogs What textual information are you analyzing or do you plan to analyze?
  • 31. Text Analytics: An Industry View JADT – June 5, 2014 34 Current, 33% Current, 31% Current, 34% Current, 47% Current, 51% Current, 56% Current, 47% Current, 54% Current, 66% Expect, 21% Expect, 24% Expect, 23% Expect, 23% Expect, 28% Expect, 25% Expect, 33% Expect, 28% Expect, 22% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Events Semantic annotations Other entities – phone numbers, part/product… Metadata such as document author,… Concepts, that is, abstract groups of entities Named entities – people, companies,… Relationships and/or facts Sentiment, opinions, attitudes, emotions,… Topics and themes Do you currently need (or expect to need) to extract or analyze...
  • 32. Text Analytics: An Industry View JADT – June 5, 2014 35 “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/
  • 33. Text Analytics: An Industry View JADT – June 5, 2014 36 10% 1% 16% 9% 36% 34% 2% 2% 18% 7% 4% 3% 13% 8% 7% 38% 3% 2% 3% 2% 5% 9% 17% 3% 28% 7% 17% 24% 2% 10% 11% 15% 8% 4% 17% 21% 3% 20% 4% 0% 1% 1% 2% 0% 0% 10% 20% 30% 40% 50% 60% Arabic Bahasa Indonesia or Malay Chinese Dutch French German Greek Hindi, Urdu, Bengali, Punjabi, or… Italian Japanese Korean Polish Portuguese Russian Scandinavian or Baltic Spanish Turkish or Turkic Other African Other Arabic script (including Urdu,… Other East Asian Other European or Slavic/Cyrillic Other Current Within 2 years Non-English language support?
  • 34. Text Analytics: An Industry View JADT – June 5, 2014 37 Software & platform options Text-analytics options may be grouped in general classes. • Installed text-analysis application, whether desktop or server or deployed in-database. • Data mining workbench. • Hosted. • Programming tool. • As-a-service, via an application programming interface (API). • Code library or component of a business/vertical application, for instance for CRM, e-discovery, search. Text analytics is frequently embedded in search or other end-user applications. The slides that follow next will present leading options in each category except Hosted…
  • 35. Text Analytics: An Industry View JADT – June 5, 2014 38 22% 25% 28% 30% 32% 33% 33% 36% 37% 40% 41% 43% 44% 45% 53% 53% 54% 64% 0% 10% 20% 30% 40% 50% 60% 70% media monitoring/analysis interface hosted or Web service (on-demand "API") option supports data fusion / unified analytics sector adaptation (e.g., hospitality, insurance, retail, health care,… BI (business intelligence) integration ability to create custom workflows or to create or change… big data capabilities, e.g., via Hadoop/MapReduce predictive-analytics integration open source support for multiple languages sentiment scoring "real time" capabilities low cost deep sentiment/emotion/opinion/intent extraction document classification broad information extraction capability ability to use specialized dictionaries, taxonomies, ontologies, or… ability to generate categories or taxonomies What is important in a solution? 2014 (n=139) 2011 (n=136) 2009 (n=78)
  • 36. Text Analytics: An Industry View JADT – June 5, 2014 39 User decision criteria Primary considerations include – Adaptation or specialization: To a business or cultural domain, language, information type (e.g., text, speech, images) & source (e.g., Twitter, e-mail, online news). By-user customization possibilities: For instance, via custom taxonomies, rules, lexicons. Sentiment resolution: Aggregate, message, or feature level. (What features? Topics, coreferenced entities?) What sentiment? Valence & what else? Emotion? Intent? Outputs: E.g., annotated text, models, indicators, dashboards, exploratory data interfaces. Usage mode: As-a-service (API), installed, or hosted/cloud. Capacity: Volume, performance, throughput, latency. Cost.
  • 37. Text Analytics: An Industry View JADT – June 5, 2014 40 A few French companies
  • 38. Text Analytics: An Industry View JADT – June 5, 2014 41 Academic spin-offs People Pattern
  • 39. Text Analytics: An Industry View JADT – June 5, 2014 42 Text analytics future: Synthesis and sensemaking.
  • 40. New York Times, September 8, 1957
  • 41. Text Analytics: An Industry View JADT – June 5, 2014 44 Emotion in text
  • 42. Text Analytics: An Industry View JADT – June 5, 2014 45 Emotion and outcomes
  • 43. Text Analytics: An Industry View JADT – June 5, 2014 46 Audio including speech. Images. Video. 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
  • 44. Text Analytics: An Industry View JADT – June 5, 2014 47 The world of big data Machine data (e.g., logs, sensor outputs, clickstreams). Actions, interactions, and transactions: geolocation and time. Profiles: individual, demographic & behavioral. Text, audio, images, and video. Facts and feelings.
  • 45. Text Analytics: An Industry View JADT – June 5, 2014 48 (Accessible) data everywhere
  • 46. Text Analytics: An Industry View JADT – June 5, 2014 49 http://www.geeklawblog.com/2011/12/lexis-advance-platform-launch-two.html A big data analytics architecture (example)
  • 47. Text Analytics: An Industry View JADT – June 5, 2014 50 http://searchuserinterfaces.com/ “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
  • 48. Text Analytics: An Industry View JADT – June 5, 2014 51 http://www.businessweek.com/magazine/content/04_19/b3882029_mz072.htm En route
  • 49. Text Analytics Past, Present & Future: An Industry View Seth Grimes Alta Plana Corporation @sethgrimes June 5, 2014