Sentiment Analysis and Applications in the News and Media Industry

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This is the talk I gave to the Financial Times on 12 September 2013 on Sentiment Analysis and applications in the news and media industry.

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Sentiment Analysis and Applications in the News and Media Industry

  1. 1. Robin Leonard CEO, AllFamous Digital robin_allfamous robinleonard1 SENTIMENT ANALYSISand applications in the news and media industry
  2. 2. What is the ROI of your mother? http://www.youtube.com/watch?v=xZY5b85KoOU
  3. 3. Agenda 1.  Why Analyze Sentiment? 2.  Sentiment Analysis 101 3.  Practical Applications
  4. 4. Why Analyze Sentiment?
  5. 5. Emotions are core to purchase decisions vs. http://www.today.mccombs.utexas.edu/2010/04/do-you-make-buying-decisions-based-on-logic- or-emotion-a-tale-of-two-chickens
  6. 6. Social Media creates lots of data Just waiting to become useful information
  7. 7. Consumers are FINALLY empowered
  8. 8. The Anatomy of a Crisis – The Incident
  9. 9. The Anatomy of a Crisis – The Impact
  10. 10. The Anatomy of a Crisis – The Response
  11. 11. The Anatomy of a Crisis – The Result
  12. 12. http://adage.com/article/digital/mckinsey-finds-social-buzz-affect-sales-negatively/242039/ Yes, Sentiment Impacts Sales
  13. 13. Agenda 1.  Why Analyze Sentiment? 2.  Sentiment Analysis 101 3.  Practical Applications
  14. 14. What is Sentiment?
  15. 15. What is Sentiment? emotions opinions intent Subjective impressions, not facts
  16. 16. Example 1: Happy Mention subject emotion brand product service
  17. 17. Example 2: Unhappy Mention fact fact opinion emotion
  18. 18. Example 3: Sarcastic Mention brand brand opinion service emotion
  19. 19. Example 4: Mixed Mention emotion service brand opinion
  20. 20. Example 5: Language Confusion brand opinion fact
  21. 21. Example 6: Intent to Buy fact brand subject intent brand brand question
  22. 22. The elusive hunt for binary •  For vs. Against •  Like vs. Dislike •  Good vs. Bad •  Ecstatic vs. Happy •  Excited vs. Amused •  ROFL vs. LOL •  Depressed vs. Unhappy •  Sad vs. Destitute •  Frustrated vs. Angry
  23. 23. Challenges of Automated Sentiment Analysis §  People express opinions in complex ways §  Cultural ideosyncracies (e.g. differences in sarcasm, irony etc) §  The wider you throw your net and the more complex the language, the less accurate the system will be.
  24. 24. Agenda 1.  Why Analyze Sentiment? 2.  Sentiment Analysis 101 3.  Practical Applications
  25. 25. Use Case 1: Get to the heart of the News In real time listen for spikes in mention volumes to identify the latest news stories
  26. 26. Use Case 2: Faster Content Crowdsourcing Once there is a breaking story, quickly identify all the images, videos and public created content on a topic
  27. 27. Use Case 3: Opinion Mining
  28. 28. Use Case 4: Reputation Management
  29. 29. Use Case 5: Consumer Insights
  30. 30. Use Case 6: Competitive Intelligence Get real-time insights on your competitors - Sun Tzu “Know your enemy and know yourself and you can fight a hundred battles without disaster”
  31. 31. Use Case 7: Brand Monitoring
  32. 32. Social Media sentiment is the #nofilter voice of the people Conclusion
  33. 33. Are you listening?
  34. 34. Thank You!! www.allfamous.com Robin Leonard CEO, AllFamous Digital robin_allfamous robinleonard1
  35. 35. References §  http://www.lct-master.org/files/MullenSentimentCourseSlides.pdf §  http://www.slideshare.net/mcjenkins/how-sentiment-analysis- works#btnNext §  http://en.wikipedia.org/wiki/Sentiment_analysis §  http://mashable.com/2010/04/19/sentiment-analysis/ §  http://www.today.mccombs.utexas.edu/2010/04/do-you-make- buying-decisions-based-on-logic-or-emotion-a-tale-of-two-chickens §  http://adage.com/article/digital/mckinsey-finds-social-buzz-affect- sales-negatively/242039/

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