Emotion identification in FIFA world cup tweets using convolutional neural network
Date Issued
2015-11-01
Author(s)
Stojanovski, Dario
Strezoski, Gjorgji
Madjarov, Gjorgji
Abstract
Twitter has gained increasing popularity over the
recent years with users generating an enormous amount of data
on a variety of topics every day. Many of these posts contain
real-time updates and opinions on ongoing sports games. In this
paper, we present a convolutional neural network architecture
for emotion identification in Twitter messages related to sporting
events. The network leverages pre-trained word embeddings
obtained by unsupervised learning on large text corpora. Training
of the network is performed on automatically annotated tweets
with 7 emotions where messages are labeled based on the presence
of emotion-related hashtags on which our approach achieves
55.77% accuracy. The model is applied on Twitter messages for
emotion identification during sports events on the 2014 FIFA
World Cup. We also present the results of our analysis on three
games that had significant impact on Twitter users.
recent years with users generating an enormous amount of data
on a variety of topics every day. Many of these posts contain
real-time updates and opinions on ongoing sports games. In this
paper, we present a convolutional neural network architecture
for emotion identification in Twitter messages related to sporting
events. The network leverages pre-trained word embeddings
obtained by unsupervised learning on large text corpora. Training
of the network is performed on automatically annotated tweets
with 7 emotions where messages are labeled based on the presence
of emotion-related hashtags on which our approach achieves
55.77% accuracy. The model is applied on Twitter messages for
emotion identification during sports events on the 2014 FIFA
World Cup. We also present the results of our analysis on three
games that had significant impact on Twitter users.
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