Title
Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types
Author
Flora Poecze
University of Applied Sciences Burgenland
Abstract
This paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.
Keywords
Social media metricsSelf-marketingSentiment analysisOnline gaming
Object type
Language
English [eng]
Persistent identifier
Appeared in
Title
Personal and Ubiquitous Computing
Publication
Springer Science and Business Media LLC
Version type
Date issued
2019
Access rights
Rights
Rights statement
(c) The Author(s) 2019
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