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0.50 0.55 0.60 0.65 0.70 0.75 0.80 AMT workers vs. Moviegoers YouTube comments vs. Moviegoers Cosine Similarity adjectives nouns Mining Emotions in Short Films User Comments or Crowdsourcing? Extract emotions in short films Exploit film criticism expressed through YouTube comments Task Create a profile for each short film Extract the terms from the profile Associate to each term an emotion and polarity Compute the emotion vector and polarity Emotion detection approach [2] 1. 2. 3. 4. Emotion lexicon Motivation Emotions are everywhere Many applications and diverse disciplines can benefit from mining emotions Human-provided word-emotion association ratings annotated according to Plutchik’s psychoevolutionary theory (NRC Emotion Lexicon - EmoLex)[1] TROPFEST YOUR FILM FESTIVAL c 1 short film comments EmoLex short film profile emotion and polarity vector Amazon Mechanical Turk Sandbox Amazon Mechanical Turk emotion and polarity vector emotion and polarity vector Cosine similarity between the emotional vectors built from expert judgments and the ones built (i) through crowdsourcing using AMT, and (ii) automatically using YouTube comments. c 2 c n c 1 c 2 c n . . . Claudia Orellana-Rodriguez [email protected] Ernesto Diaz-Aviles [email protected] Wolfgang Nejdl [email protected] Plutchik’s Wheel of Emotions Claudia Orellana-Rodriguez L3S Research Center e-mail: [email protected] [1] S. M. Mohammad and P. D. Turney, “Crowdsourcing a word- emotion association lexicon,” Computational Intelligence, 2011. [2] E. Diaz-Aviles, C. Orellana-Rodriguez, and W. Nejdl. Taking the Pulse of Political Emotions in Latin America Based on Social Web Streams. In LA-WEB, 2012
