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
Search Results
Do Consumer Perceptions of Tanking Impact Attendance at National Basketball Association Games? A Sentiment Analysis Approach
Hua Gong, Nicholas M. Watanabe, Brian P. Soebbing, Matthew T. Brown, and Mark S. Nagel
Big Data and Innovative Research Methods
Yoseph Z. Mamo
, particularly in the areas of communication, marketing, management, and sales. For instance, for customer segmentation, techniques such as K-means and Gaussian mixture models, as well as NLP, and sentiment analysis techniques such as Naive Bayes, support vector machines, and deep learning techniques were used
Through the Perilous Fight: A Case Analysis of Professional Wrestling During the COVID-19 Pandemic
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
From Gearshifts to Gigabytes: An Analysis of How NASCAR Used iRacing to Engage Fans During the COVID-19 Shutdown
Greg Greenhalgh and Chad Goebert
followers as a critical form of economic demand for sport organizations. Sentiment Analysis The analysis of social media, specifically Twitter, during a televised event has served as an informative way for researchers to better understand the emotional responses viewers have about the event they are
Using Profanity and Negative Sentiments: An Analysis of Ultimate Fighting Championship Fighters’ Trash Talk on Fans’ Social Media Engagement and Viewership Habits
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
Making an Impact: An Initial Review of U.S. Sport League Corporate Social Responsibility Responses During COVID-19
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
Examining the Intersection of Sport, Social Media, and Crisis Communication
Evan L. Frederick and Ann Pegoraro
responsibility responses among U.S. sport leagues during COVID-19. Data were gathered via qualitative interviews, secondary data, and social media sentiment analysis. Interviews and secondary research revealed three key themes: (a) educate , (b) assist, and (c) inspiration . Fan sentiment data were gathered
Using Artificial Intelligence to Detect the Relationship Between Social Media Sentiment and Season Ticket Purchases
Nels Popp, James Du, Stephen L. Shapiro, and Jason M. Simmons
) have utilized a more simplistic measure of user sentiment (e.g., positive, negative, neutral), the current research sought a more nuanced taxonomy to assess sentiment, which is explored in further detail in the following section. Emotions and Sentiment Analysis Emotions are frequently conceptualized as
Let Us Debate! A Proposal to Promote Social Entrepreneurship in Physical Education Teacher Education
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
The State of Quantitative Research and a Proposed Research Framework in Social Media
Thilo Kunkel, Heather Kennedy, Bradley J. Baker, and Jason P. Doyle
(including variants such as analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance). Structural Equation Modeling and purely descriptive analysis have become less common, and a diversification of techniques (e.g., sentiment analysis or support vector analysis) has
