1. Sentiment Analysis Applied Advertising & Public Relations Research JOMC 279

7. "Listening is the study of naturally occurring conversations, behaviors, and signals—information that may or may not be guided—that brings the voice of people's lives in to a brand."

8. Why Do Brands Listen? Insights (wants, unmet needs, challenges) Voice of consumer Redefine relationships Understand shifts in perspectives Understand context & reasons why

9. Where Do Brands Listen? Offline – Comment cards – Trade-show notes – CRM / sales mgmt. systems Online – Brand backyard – Customer backyard

10. Whom Do Brands Listen To? Customers Prospects Business partners Friends, contacts, followers Others

11. How Do Brands Make Sense of What They Hear? Search & Monitoring Text Analytics Full-Service Listening Platforms Private Communities

13. Measuring what your customers say about you when they're talking to each other. LISTENING

14. Advantages (Online) Unobtrusiveness Immediate / Real-time Natural, rich, unfiltered WOM BIG data

15. Disadvantages (Online) Ethics Representativeness / Accuracy WOM Noise BIG data

16. Sentiment Analysis aka “opinion mining” Measurement of emotion in texts – Polarity – Strength Human coding vs. NLP Methodological standards / transparency

19. Project 2 Results Data set: You were provided with 200 Tweets related to pizza. (2 sets) Code each Tweet as – Positive, Negative, Mixed, or Neutral. When coded as Positive, Negative, or Mixed, identify the portion of the Tweet that resulted in that decision. Evaluate the difficulty of the coding decision.

20. Natural Language Processing SocialRadar vs. SentiStrength Observed agreement =.315 – Both data sets Why would computing kappa be inappropriate in this situation?

21. OAkappa Sentistrength0.6800.502 Sentistrength0.6000.424 Sentistrength0.5850.374 Sentistrength0.5100.314 Sentistrength0.5000.309 Sentistrength0.4850.307 Sentistrength0.4150.150 Social Radar0.4600.211 Social Radar0.4500.151 Social Radar0.4450.203 Social Radar0.4000.207 Social Radar0.3800.184 Social Radar0.3650.188 Social Radar0.3200.172

22. “After coding these tweets, it is easy to see why computers might not be the most effective way for a brand or company to decipher customers’ tweets about a product or service.”

23. “I have come to admire people who are professional coders.” But are humans better?

24. OAkappa 0.6600.487 0.5550.295 0.5250.310 0.8100.712 0.7000.556 0.6700.519 0.6650.513 0.6650.526 0.6650.512

25. Difficulty correlations 0.397 0.381 0.358 0.344 0.338 0.238 0.229 0.160 0.078