@article {1457,
	title = {Using Web Text Analytics to Categorize the Business Focus  of Innovative Digital Health Companies},
	journal = {Technology Innovation Management Review},
	volume = {11},
	year = {2021},
	month = {10/2021},
	pages = {65-78},
	publisher = {Talent First Network},
	chapter = {65},
	address = {Ottawa},
	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},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1457},
	url = {timreview.ca/article/1457},
	author = {Abdulla Aweisi and Daman Arora and Ren{\'e}e Emby and Madiha Rehman and George Tanev and Stoyan Tanev}
}
@article {1082,
	title = {Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors},
	journal = {Technology Innovation Management Review},
	volume = {7},
	year = {2017},
	month = {06/2017},
	pages = {25-37},
	publisher = {Talent First Network},
	address = {Ottawa},
	abstract = {In this article, we describe our efforts to adapt and validate a web search and analytics tool {\textendash} the Gnowit Cognitive Insight Engine {\textendash} to evaluate the growth and competitive potential of new technology startups and existing firms in the newly emerging precision medicine sector. The results are based on two different search ontologies and two different samples of firms. The first sample includes established drug companies operating in the precision medicine field and was used to estimate the relationship between the firms{\textquoteright} innovativeness and the extent of online discussions focusing on their potential growth. The second sample includes new technology firms in the same sector. The firms in the second sample were used as test cases to determine whether their growth-related web search scores would relate to the degree of their innovativeness. The second part of the study applied the same methodology to the real-time monitoring of the firms{\textquoteright} competitive actions. In our findings, we see that our methodology reveals a moderate degree of correlation between the Insight Engine{\textquoteright}s algorithmically computed relevance scores and independent measures of innovation potential. The existence of such correlations invites future work in attempting to analyze company growth potential using techniques founded in web content scraping, natural language processing, and machine learning.},
	keywords = {analytics, artificial intelligence, business intelligence, entrepreneurship, online textual data, precision medicine sector, startup growth potential},
	issn = {1927-0321},
	doi = {http://doi.org/10.22215/timreview/1082},
	url = {http://timreview.ca/article/1082},
	author = {Andrew Droll and Shahzad Khan and Ehsanullah Ekhlas and Stoyan Tanev}
}
