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Hua Gong, Nicholas M. Watanabe, Brian P. Soebbing, Matthew T. Brown, and Mark S. Nagel

lens. One of the approaches emerging in recent years is the use of sentiment analysis on large volumes of social media data as a way to improve the understanding of individuals or specific interest groups ( Fang & Zhan, 2015 ). In sport management research, there has, likewise, been a recent increase

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Nicholas P. Davidson, James Du, and Michael D. Giardina

WWE’s latest video game release (#WWE2k20 and #Gaming). Figure 1 —Text-mining results of Twitter contents. Figure  2 displays the results of a sentiment analysis, measuring the extent to which the eight emotional themes (i.e., anger, anticipation, disgust, fear, joy, sadness, surprise, and trust

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Duarte Tereso, Sérgio Moro, Pedro Ramos, Teresa Calapez, Joana M. Costa, and Tyler Ratts

cultivation of long-term relationships with fans. With the development of tools such as the application programming interface, scholars also have the ability to collect a considerable number of tweets instantaneously, which can be analyzed using sentiment analysis tools. This resource has been beneficial in

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Danielle K. Smith and Jonathan Casper

feel about their league’s respective communication outreach. The data for this commentary were gathered using qualitative interviews with select league CSR leaders, secondary research on league communication programs, and social media sentiment analysis (SA). These data provide an initial glance at the

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Carlos Capella-Peris, Oscar Chiva-Bartoll, Celina Salvador-Garcia, and María Maravé-Vivas

studies ( Capella-Peris, Gil-Gómez, & Chiva-Bartoll, 2020 ). Likewise, there are also precedents for sentiment analysis in mixed-methods research ( Salvador-García et al., 2020 ). Data Analysis For quantitative analysis, Cronbach’s alpha test, Levene’s test, the t test, and Pearson’s test were performed

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Elaine Chiao Ling Yang, Michelle Hayes, Jinyan Chen, Caroline Riot, and Catheryn Khoo-Lattimore

tweets. Figure 1 —Steps of creating female and male data collection. Phase 2: Sentiment Analysis Sentiment analysis was conducted to make an overall comparison between female and male data sets. The sentiment analysis method transfers subjective text to quantitative data and delivers information about

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Orland Hoeber, Ryan Snelgrove, Larena Hoeber, and Laura Wood

Alberto Contador. Figures  1 – 3 show the sentiment analysis of crashes for these three riders, respectively. Of note, there was a strong negative sentiment regarding Cavendish’s crash in 2014. Thus, we decided to explore this incident in more detail. Figure 1 Sentiment analysis from Vista of “crash” and

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Brian A. Eiler, Rosemary Al-Kire, Patrick C. Doyle, and Heidi A. Wayment

not to use it here as one of our primary goals was to ensure that negation was accounted for and that our language processing utilized a domain-independent machine learning algorithm rather than a frequency-based approach. 1 The recently developed SEntiment ANalysis and Cognition engine by Crossley

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Liam Kennedy, Derek Silva, Madelaine Coelho, and William Cipolli III

. We focus on 126,393 tweets in English for our analyses (see Figure  1 ). A time series plot of the tweets, created using the “ggplot2” package ( Wickham, 2016 ) for R ( R Core Team, 2018 ), can be found in Figure  2 . For the following programmatically completed sentiment analysis, we make use of the

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Nicholas M. Watanabe, Stephen Shapiro, and Joris Drayer

, and Nagel ( 2021 ) investigated the impact of tanking in the National Basketball Association through sentiment analysis. The authors examined big data through a multidimensional lens by emphasizing the concept of variety within sport analytics research. Not only did this study examine patterns of