Technology Reads Audience Reactions to Republican Primary Debate
Emotient, which helps organizations better understand audience response to media, messages, and experiences via proprietary emotion-reading algorithms and analytics, measured Friday the reactions of an independently selected studio audience viewing the first Republican Presidential Primary Debate aired on FOX News.
Emotient’s software reads the expressions of individuals and crowds to gain insights and assess authentic emotions and reactions to stimuli, such as a message, a person or an experience.
The technology is able to verify whether a crowd is feeling fear, contempt, disgust, joy, anger, surprise or sadness at a particular moment.
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During the course of the two-hour debate, Emotient’s facial reading algorithm recorded the visual reactions of a 60-person opt-in audience, and identified the following emotional highlights and responsive moments of the debate:
- An almost 90% spike in audience “joy” surfaced during the exchange between Donald Trump and Rand Paul, with Trump stating “I don’t think you heard me. You’re having a hard time tonight.”
- High levels of “contempt”, “anger” and “disgust” were measured among viewers when Scott Walker was questioned on his promise to create 250,000 Wisconsin jobs.
- Significant levels of “frustration”, “anger” and “confusion” revealed themselves in response to Jeb Bush’s suggestion that the Iraq war a mistake.
Statistically, the most “joyous” moments were attributed to Donald Trump, with him eliciting three out of the top five “joy” spikes during the debate.
Scott Walker evoked the highest degree of “anger,” and the most “disgust” surfaced during the Chris Christie-Rand Paul exchange over surveillance. Conversely, Ted Cruz seemingly failed to provoke any significant emotional response from Emotient’s studio audience.
“Emotient is the only company in the world that can – and has – applied emotion reading technology to a political event to verify what audiences are truly feeling about a candidate, an issue or an exchange in real time,” said Emotient CEO Ken Denman.
“Our technology is accurate where America’s best-known public opinions gurus fail, because it is a neutral, machine learning system with one job: to mathematically categorize the physical expression of emotions without dogma, without party affiliation and without prejudice.”
Emotient, Inc., offers technology for facial expression analysis. Emotient software translates facial expressions into actionable information that helps companies make better decisions based on audience response to media, products and experiences.
Emotient facial expression technology is currently available through the EmotientAnalytics web service or the Emotient API. Automated emotion measurement can generate insights that increase revenue across many industries including advertising, media and entertainment, consumer goods, retail, enterprise sales, healthcare and education.
Emotient was founded by a team of six PhDs from the University of California, San Diego, who specialize in the application of machine learning, computer vision and cognitive science to facial behavioral analysis.