Using Web Text Analytics to Categorize the Business Focus of Innovative Digital Health Companies

Abstract

Categorizing the market focus of larger samples of companies can be a tedious and time-consuming process for both researchers and business analysts interested in developing insights about emerging business sectors. The objective of this article is to suggest a text analytics approach to categorizing the application areas of companies operating in the digital health sector based on the information provided on their websites. More specifically, we apply topic modeling on a collection of text documents, including information collected from the websites of a sample of 100 innovative digital health companies. The topic model helps in grouping the companies offering similar types of market offers. It enables identifying the companies that are most highly associated with each of the topics. In addition, it allows identifying some of the emerging themes that are discussed online by the companies, as well as their specific market offers. The results will be of interest to inspiring technology entrepreneurs, organizations supporting new ventures, and business accelerators interested to enhance their services to new venture clients. The development, operationalization, and automation of the company categorization process based on publicly available information is a methodological contribution that opens the opportunity for future applications in research and business practice.

Keywords

• digital health sector
• machine learning
• market offer
• topic modeling algorithm
• value proposition
• web analytics