Automated text analyses of YouTube comments as field experiments for assessing consumer sentiment towards products and brands

Abstract

PurposeThe purpose of this study is to show how non-random groupings of YouTube videos can be combined with automated text analysis (ATA) of user comments to conduct quasi-experiments on consumer sentiment towards different types of brands in a naturalistic setting.Design/methodology/approachNCapture extracted thousands of comments on multiple videos representing different experimental treatments and Leximancer revealed differences in the lexical patterns of user comments for different types of brands.FindingsUser comments consistently revealed hypothesized relationships between brand types, based on existing theory regarding motivations for nostalgia and the relationship between consumer preferences, online product ratings and purchases. These results demonstrate the viability of conducting quasi-experimental research in naturalistic settings via non-random groupings of YT videos and ATA of user comments.Research limitations/implicationsThis research adopts a single quasi-experimental design: the non-equivalent group, after-only design. However, the same basic approach can be used with other quasi-experimental designs to examine different kinds of research questions.Originality/valueOverall, this research points to the potential for ATA of comments on different categories of YT videos as a relatively straightforward approach for conducting field experiments that establish the ecological validity of laboratory findings. The method is easy to use and does not require the participation and cooperation of private, third party social media research companies.