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Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
Oscar twitter geo_sentiment
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Oscar twitter geo_sentiment

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Transcript

  • 1. Monitoring Real Time Market Sentiment During the Academy Awards Through Twitter<br />
  • 2. Temporal animation<br />Pivot across variables<br />Live filtering<br />Sentiment over time<br />On click geographic details<br />
  • 3. Each Tweet’s sentiment is calculated between +1 and -1 then the total sentiment for the movie is calculated for before, during and after the Oscars. The larger the number to greater the positive (+) or negative (-) sentiment for the movie. The score is indicative of both overall sentiment as well as the volume of people expressing the sentiment<br />
  • 4. Heavy Negative Sentiment for “Black Swan”<br />
  • 5. Heavy Positive Sentiment for “True Grit”<br />
  • 6. Mixed Sentiment for “127 Hours”<br />
  • 7. Each Tweet’s sentiment is calculated between +1 and -1 then the total sentiment for the actor/actress is calculated for before, during and after the Oscars. The larger the number to greater the positive (+) or negative (-) sentiment for the actor/actress. The score is indicative of both overall sentiment as well as the volume of people expressing the sentiment<br />
  • 8. Largely Positive Sentiment for “Jeff Bridges”<br />
  • 9. Largely Negative Sentiment for “Nichole Kidman”<br />
  • 10. Mixed Sentiment for “James Franco”<br />
  • 11. Black Swan<br />Negative Sentiment for Black Swan but Positive Sentiment for its Actress “Natalie Portman”<br />Natalie Portman<br />
  • 12. Reaction to The Social Network Winning Best Score<br />
  • 13. The Reaction to “The Fighter” in the Boston Market<br />

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