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http://dx.doi.org/10.5351/KJAS.2022.35.1.147

Analysis of speech in game marketing video using text mining techniques  

Lee, Yeokyung (Department of Statistics, Sungkyunkwan University)
Kim, Jaejik (Department of Statistics, Sungkyunkwan University)
Publication Information
The Korean Journal of Applied Statistics / v.35, no.1, 2022 , pp. 147-159 More about this Journal
Abstract
Nowadays, various social media platforms are widely spread and people closely use such platforms in daily life. By doing so, social influencers with a large number of subscribers, views, and comments have huge impact in our society. Following this trend, many companies are actively using influencers for marketing purpose to promote their products and services. In this study, we extract the speeches of influencers from videos for game marketing and analyze them using various text mining techniques. In the analysis, we distinguish game videos leading to successful marketing and failed marketing, and we explore and compare the linguistic features of the influencers for successful and failed marketings.
Keywords
text mining; term frequency-inverse document frequency (TF-IDF); n-gram; latent Dirichlet allocation;
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