0.50$

0.55$

0.60$

0.65$

0.70$

0.75$

0.80$

AMT$workers$vs.$Moviegoers$ YouTube$comments$vs.$Moviegoers$

Cosine

$Sim

ilarity$

adjectives

nouns

Mining Emotions in Short FilmsUser Comments or Crowdsourcing?

Extract emotions in short filmsExploit film criticism expressed through YouTube comments

Task

Create a profile for each short filmExtract the terms from the profileAssociate to each term an emotion and polarityCompute the emotion vector and polarity

Emotion detection approach [2]1.2.3.4.

Emotion lexicon

MotivationEmotions are everywhereMany applications and diverse disciplines can benefit from mining emotions

Human-provided word-emotionassociation ratings annotatedaccording to Plutchik’s psychoevolutionarytheory (NRC Emotion Lexicon - EmoLex)[1]

TROPFEST YOUR FILMFESTIVAL

c1

short filmcomments

EmoLexshort filmprofile

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.

c2

cn

c1c2

cn

.

.

.

Claudia Orellana-Rodriguez

orellana@L3S.de

Ernesto Diaz-Aviles

diaz@L3S.de

Wolfgang Nejdl

nejdl@L3S.de

Plutchik’s Wheel of Emotions

Claudia Orellana-Rodriguez

L3S Research Center

e-mail: orellana@L3S.de

[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