Social Media Sentiments for People’s Oscar
Social media users are weighing in with their Oscar picks and pans, according to the University of Southern California Annenberg Innovation Lab, IBM, and the Los Angeles Times. They are measuring social media sentiment related to the 84th Academy Awards.
The project relies on new analytics and natural language recognition technologies to gauge positive and negative opinions shared in millions of public tweets.
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Focused on the Best Actor, Best Actress and Best Picture categories, the goal is to establish a model for measuring the volume and tone of worldwide Twitter sentiment to better understand moviegoers’ opinions. The results are intended to illuminate how advances in technology can help identify important consumer trends.
The Times has published information about the project including an infographic illustrating ongoing sentiment. The analysis will be updated at www.latimes.com/senti-meter as sentiment evolves over the weeks leading up to the Academy Awards on Feb. 26, 2012.
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“This project is about identifying ‘The People’s Oscar,’ which means moving beyond pundits’ opinions of who the winners may be, to understanding who real moviegoers want to see receive the highest accolades of the industry,” said Professor Jonathan Taplin, director of the USC Annenberg Innovation Lab. “We want to illustrate how new technologies can capture valuable information and opinions derived from the voices of influential movie fans.”
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The work between the USC Annenberg Innovation Lab and IBM is part of an ongoing collaboration to explore how technology can be used by organizations from news outlets and journalists, to movie studios and retailers, in order to better understand, respond, and predict public sentiment.
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In the case of the Oscar race, analytics and language software from IBM is being used that distinguishes nuance and sarcasm in order to pinpoint relevant opinions of the nominated films, actors and actresses and show noteworthy trends.
To date, USC Annenberg and IBM have also applied similar techniques to film forecasting, sports and retailing in an effort to identify social media trends and better understand public opinions.