Mining Emotions in Short Films: User Comments or Crowdsourcing?
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This document discusses mining emotions from user comments on short films. It presents an approach that creates an emotion vector for each short film based on extracting terms from user comments on YouTube and associating them with emotions from the NRC Emotion Lexicon. It then compares the cosine similarity between emotion vectors built from expert judgments and those built using Amazon Mechanical Turk workers or automatically from YouTube comments. The goal is to determine if crowdsourcing or YouTube comments can accurately extract emotions expressed in reviews of short films.
![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](https://image.slidesharecdn.com/posterwww13orellana-131122063703-phpapp01/85/Mining-Emotions-in-Short-Films-User-Comments-or-Crowdsourcing-1-320.jpg)
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Mining Emotions in Short Films: User Comments or Crowdsourcing?
- 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