GOAALLL!: Using Sentiment in the World Cup to Explore Theories of Emotion (bibtex)
by Gale M. Lucas, Jonathan Gratch, Nikolaos Malandrakis, Evan Szablowski, Eli Fessler, Jeffrey Nichols
Abstract:
Sporting events evoke strong emotions amongst fans and thus act as natural laboratories to explore emotions and how they unfold in the wild. Computational tools, such as sentiment analysis, provide new ways to examine such dynamic emotional processes. In this article we use sentiment analysis to examine tweets posted during 2014 World Cup. Such analysis gives insight into how people respond to highly emotional events, and how these emotions are shaped by contextual factors, such as prior expectations, and how these emotions change as events unfold over time. Here we report on some preliminary analysis of a World Cup twitter corpus using sentiment analysis techniques. After performing initial tests of validation for sentiment analysis on data in this corpus, we show these tools can give new insights into existing theories of what makes a sporting match exciting. This analysis seems to suggest that, contrary to assumptions in sports economics, excitement relates to expressions of negative emotion. The results are discussed in terms of innovations in methodology and understanding the role of emotion for “tuning in” to real world events. We also discuss some challenges that such data present for existing sentiment analysis techniques and discuss future analysis.
Reference:
GOAALLL!: Using Sentiment in the World Cup to Explore Theories of Emotion (Gale M. Lucas, Jonathan Gratch, Nikolaos Malandrakis, Evan Szablowski, Eli Fessler, Jeffrey Nichols), In Image and Vision Computing, 2017.
Bibtex Entry:
@article{lucas_goaalll!:_2017,
	title = {{GOAALLL}!: {Using} {Sentiment} in the {World} {Cup} to {Explore} {Theories} of {Emotion}},
	issn = {02628856},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0262885617300148},
	doi = {10.1016/j.imavis.2017.01.006},
	abstract = {Sporting events evoke strong emotions amongst fans and thus act as natural laboratories to explore emotions and how they unfold in the wild. Computational tools, such as sentiment analysis, provide new ways to examine such dynamic emotional processes. In this article we use sentiment analysis to examine tweets posted during 2014 World Cup. Such analysis gives insight into how people respond to highly emotional events, and how these emotions are shaped by contextual factors, such as prior expectations, and how these emotions change as events unfold over time. Here we report on some preliminary analysis of a World Cup twitter corpus using sentiment analysis techniques. After performing initial tests of validation for sentiment analysis on data in this corpus, we show these tools can give new insights into existing theories of what makes a sporting match exciting. This analysis seems to suggest that, contrary to assumptions in sports economics, excitement relates to expressions of negative emotion. The results are discussed in terms of innovations in methodology and understanding the role of emotion for “tuning in” to real world events. We also discuss some challenges that such data present for existing sentiment analysis techniques and discuss future analysis.},
	journal = {Image and Vision Computing},
	author = {Lucas, Gale M. and Gratch, Jonathan and Malandrakis, Nikolaos and Szablowski, Evan and Fessler, Eli and Nichols, Jeffrey},
	month = jan,
	year = {2017},
	keywords = {UARC, Virtual Humans}
}
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