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Title:
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TWITTER SENTIMENT ANALYSIS: FAN ENGAGEMENT
IN ESPORTS MATCHES |
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Author(s):
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Sarah Anne Yan and Peter Mawhorter |
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ISBN:
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978-989-8704-19-1 |
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Editors:
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Piet Kommers and Guo Chao Peng |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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Esports, Twitter, Sentiment Analysis, Polarity Analysis, Flashbulb Memory, Python |
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Type:
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Short |
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First Page:
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257 |
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Last Page:
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261 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Sentiment analysis is used to gain an understanding of the opinions, emotions, and subjectivity of text. Twitter is a social
networking service where millions of users post and interact with messages. For multiplayer video game competitions,
known as esports, many fans use Twitter as a platform to react to the match progress and results. In this study, we utilize
the Twitter API and analyze tweets relating to Overwatch League matches using TextBlob to compute the sentiment
polarity based on POS tagging. Each tweet will be assigned a score in the range of -1 to +1. We hypothesized that tweets
mentioning winning teams would include more positive sentiments on average than tweets mentioning losing teams, but
our preliminary analysis (with limited data) indicates that the relationship between sentiment and match outcomes may be
more complicated. |
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