Defining Virtual Consumerism Through Content and Sentiment Analyses.

This study set out to better understand virtual consumerism (VC) by applying natural language processing (NLP) methods for sentiment and content analyses. A total of 318 articles related to VC were identified on Web site and analyzed by text mining methodology. A thematic, content analysis using the Leximancer program was performed to explore VC as a concept, and its related concepts and concept associations. For the purposes of "deep-dive insights," further content and sentiment analyses were performed with MonkeyLearn and valence aware dictionary for sentiment reasoning. This triangulation in methodology enabled a comprehensive unstructured qualitative data analysis. The study identified key themes that characterize and define VC. It uncovered that, although there is predominantly positive sentiment toward VC reported in The Guardian online articles, negative sentiment also exists, presenting challenges for the industry to maneuver. The findings reveal that in the context of VC, a virtual experience is also a social experience in a virtual space, which is becoming and evolving. There are certain industries and sectors that are embracing VC, such as marketing, advertising and public relations, software development/IT, art/design, and entertainment, as well as science/technology. Some sectors and industries are experiencing challenges, such as security/law enforcement and medical, and hence display negative sentiment toward VC. Overall, this study presents a working definition of VC, a synopsis of the state of VC, and highlights areas for potential research to further our understanding of this phenomenon. It contributes to an improved understanding of VC for the industry and academia, and provides impetus for future studies focused on the emergent VC-relevant conceptual relationships.