Mining Emotions in Short Films
User Comments or Crowdsourcing?

Claudia Orellana-Rodriguez

Ernesto Diaz-Aviles

Wolfgang ...
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Mining Emotions in Short Films: User Comments or Crowdsourcing?

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CUbRIK research poster presented at WWW 2013 by L3S

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Mining Emotions in Short Films: User Comments or Crowdsourcing?

  1. 1. Mining Emotions in Short Films User Comments or Crowdsourcing? Claudia Orellana-Rodriguez Ernesto Diaz-Aviles Wolfgang Nejdl orellana@L3S.de diaz@L3S.de nejdl@L3S.de Motivation Task Emotions are everywhere Many applications and diverse disciplines can benefit from mining emotions Extract emotions in short films Exploit film criticism expressed through YouTube comments Emotion detection approach [2] Emotion lexicon Human-provided word-emotion association ratings annotated according to Plutchik’s psychoevolutionary theory (NRC Emotion Lexicon - EmoLex)[1] 1. Create a profile for each short film 2. Extract the terms from the profile 3. Associate to each term an emotion and polarity 4. Compute the emotion vector and polarity Plutchik’s Wheel of Emotions TROPFEST YOUR FILM FESTIVAL c1 c2 cn short film comments c1 c2 . . . cn short film profile 0.80$ Amazon Mechanical Turk Sandbox emotion and polarity vector emotion and polarity vector emotion and polarity vector adjectives EmoLex 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. 0.75$ Cosine$Similarity$ Amazon Mechanical Turk nouns 0.70$ 0.65$ 0.60$ 0.55$ 0.50$ AMT$workers$vs.$Moviegoers$ YouTube$comments$vs.$ Moviegoers$ [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 Claudia Orellana-Rodriguez L3S Research Center e-mail: orellana@L3S.de

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