Newswise — Super Bowl advertising was as big as the game itself; and this year, more than any other, these advertisements were part of the conversation in social media.
Mayukh Dass, assistant professor of marketing in Texas Tech University’s Rawls College of Business, Piyush Kumar, of the University of Georgia and the Wharton School, and Shyam Kapur, president of TipTop Technologies, a Silicon Valley startup, tested a novel system to track and analyze the buzz created in the blogosphere by Super Bowl advertisements. They used their new system to assess the emotional quotients (EQ) of Super Bowl ads.
The complete analysis is available at Dass’ website and clicking on “superbowl advertisement analysis.” According to Dass’ data, Chrysler ads had the most tweets, while Groupon’s ads captured the highest number of sentiments. GoDaddy spots drew the highest number of “hate” tweets, Groupon drew the highest number of “anger” tweets, while Chrysler captured the highest number of “love” and Best Buy scored the highest number of “happiness” tweets. Dass said there were a couple of notable events. “Although the overall sentiment on Pepsi is positive, we observed a high surge of negative tweets after they showed a black lady hitting a white lady with a can. People felt that it was ‘racist,’” Dass said. Throughout the evening, Dass said most car advertisements failed to create any buzz, except the Chrysler 200 ad featuring Eminem, which attracted a good number of positive and negative tweets.
By using real-time data on Twitter and subjecting it to a series of analyses using sophisticated artificial intelligence, natural language processing and network analysis tools, Dass said that they may be able to show ad sponsors whether or not their ads were effective.
“This first-of-its-kind analysis gives information about the pattern of emotional response to each advertisement and provides timely clues in terms of which ones clicked with the audience and on which dimension,” Dass said.
In addition to ranking the advertisements based on the overall positive and negative sentiments generated, the analyses pinpoints specific emotions that were triggered by each advertisement and compares them on emotional intensity.
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