sentiment analysis applied advertising & public relations research jomc 279
TRANSCRIPT
"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."
Why Do Brands Listen?
• Insights (wants, unmet needs, challenges)• Voice of consumer• Redefine relationships• Understand shifts in perspectives• Understand context & reasons why
Where Do Brands Listen?
• Offline– Comment cards– Trade-show notes– CRM / sales mgmt. systems
• Online– Brand backyard– Customer backyard
Whom Do Brands Listen To?
• Customers• Prospects• Business partners• Friends, contacts, followers• Others
How Do Brands Make Senseof What They Hear?
• Search & Monitoring• Text Analytics• Full-Service Listening Platforms• Private Communities
Advantages (Online)
• Unobtrusiveness• Immediate / Real-time• Natural, rich, unfiltered WOM• BIG data
Sentiment Analysis
• aka “opinion mining”• Measurement of emotion in texts– Polarity– Strength
• Human coding vs. NLP• Methodological standards / transparency
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.
Natural Language Processing
• SocialRadar vs. SentiStrength
• Observed agreement = .315 – Both data sets
• Why would computing kappa be inappropriate in this situation?
OA kappaSentistrength 0.680 0.502Sentistrength 0.600 0.424Sentistrength 0.585 0.374Sentistrength 0.510 0.314Sentistrength 0.500 0.309Sentistrength 0.485 0.307Sentistrength 0.415 0.150
Social Radar 0.460 0.211Social Radar 0.450 0.151Social Radar 0.445 0.203Social Radar 0.400 0.207Social Radar 0.380 0.184Social Radar 0.365 0.188Social Radar 0.320 0.172
“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.”
OA kappa0.660 0.4870.555 0.2950.525 0.310
0.810 0.7120.700 0.5560.670 0.5190.665 0.5130.665 0.5260.665 0.512