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An Industry Perspective on Subjectivity, Sentiment, and Social

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Invited presentation by Seth Grimes at the 7th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, June 16, 2016

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An Industry Perspective on Subjectivity, Sentiment, and Social

  1. 1. An Industry Perspective on Subjectivity, Sentiment, and Social Seth Grimes Alta Plana Corporation @sethgrimes 7th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis June 16, 2016 – San Diego
  2. 2. Agenda Industry applications … analyst views. Commercialization illustrated. Routes to market, futures.
  3. 3. Social Media Analytics
  4. 4. Brian Solis http://www.briansolis.com/2012/04/meet-generation-c-the-connected-customer/ A Journey Metaphor
  5. 5. Dion Hinchcliffe https://www.enterpriseirregulars.com/58013/social-media-marketing-predictions-for-2013-part-1/ Subjective Judgments
  6. 6. Emotion Discovered
  7. 7. Voices
  8. 8. http://www.greenbook.org/grit Research & Insights
  9. 9. 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% Events Semantic annotations Other entities – phone numbers, part/product numbers, e-mail & street addresses, etc. Metadata such as document author, publication date, title, headers, etc. Concepts, that is, abstract groups of entities Named entities – people, companies, geographic locations, brands, ticker symbols,… Relationships and/or facts Sentiment, opinions, attitudes, emotions, perceptions, intent Topics and themes Do you currently need (or expect to need) to extract or analyze – http://altaplana.com/TA2014
  10. 10. Data Type Examples Applications Verbatims from quantitative surveys Brand trackers, customer/ employee engagement studies Advert/Product testing likes & dislikes Enterprise Feedback Management/Voice of the customer programmes What are people saying? Auto-coding of verbatim into themes What is good and bad? Measure and track sentiment overall and by themes What should we improve first? Text based impact/driver analysis for action prioritisation Real-time analysis of customer feedback Prioritise actions and interventions in timely fashion Qualitative surveys Online communities, focus group transcripts, public consultation transcripts What is being said and in what context? Extract key words and patterns in data Can we map the key themes? Visualise patterns and relationships between concepts What should wider analysis focus on? Guide/focus analysis Social media data User Generated Content from social networks, data from forums/ blogs/review sites, news sites etc... What are people saying? Auto-coding of comments into themes What is good and bad? Measure and track market sentiment What is being said about our competitors? Competitive intelligence Is there anything new we should be worrying about? Identify early signals of user/customer opinion Feedback/data held by organisations Call centre logs/recordings, data from website, email data etc.. What have people said in the past? Audit of existing data Is any of our existing data relevant for our current task? Find relevant data for further analysis What are people saying? Identify recurrent themes https://www.ipsos- mori.com/researchpublicatio ns/publications/1766/A- Guide-to-Text-Analytics.aspx
  11. 11. Industry Applications Market research & customer insights Customer experience Social engagement Brand/reputation management Recommendation systems Consumer markets Financial markets Health sciences & clinical medicine Military/intelligence
  12. 12. “Emotional Market Research”
  13. 13. Decisions What’s your core capability? What are your differentiators? Tool, application, component, or solution? Industry adaptation, e.g., hospitality, healthcare, consumer electronics: • Models. • Workflow, interfaces, analyses. NLP/information extraction capabilities. Data sources handled & data availability.
  14. 14. Core?
  15. 15. Differentiators?
  16. 16. Tool, application, component, or solution?
  17. 17. Adaptation
  18. 18. NLP/IE
  19. 19. http://www.depechemood.eu/DepecheMood.html Prove It
  20. 20. Routes to Market Academic commercialization office. Angel, venture funding. Program funding, for example EU, NSF, IARPA, or SBIR. Consulting/services funded. Alliance with an integrator/consultancy, solution provider, or customer. Platform adherence. Or get hired.
  21. 21. Academic +
  22. 22. Investors Consult: • AngelList (angel.co). • CrunchBase. • Index (index.co).
  23. 23. Dead
  24. 24. Doomed?
  25. 25. Platform/ integration
  26. 26. Get Hired
  27. 27. Sentiment futures The same, but more so: Descriptive • Integrated applications. • Integrated data (behaviors, profiles, reference). • Personas and affinities. • Images, audio, and video. • Physical affective states. • Cross-source. New(-er) stuff: Generative • Conversational interfaces. • Narrative, argumentation, translation. • Virtual environments. Evocative
  28. 28. Personas 1
  29. 29. Personas 2
  30. 30. http://www.beyondverbal.com/#wea Facial coding demo at: https://labs- portal.affectiva.com/portal/web-demo Beyond Text
  31. 31. Conversational Interfaces (+ IoT)
  32. 32. sentimentsymposium.com Start-up, government/academic, small company, and student discounts available.
  33. 33. An Industry Perspective on Subjectivity, Sentiment, and Social Seth Grimes Alta Plana Corporation @sethgrimes 7th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis June 16, 2016 – San Diego

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