02228nas a2200277   4500008004100000022001400041245010200055210006900157260004200226300001000268490000700278520139300285653002601678653002101704653001701725653002901742653002201771653001801793100002001811700001701831700001701848700001901865700001801884700001801902856003001920       2021                        eng d  a1927-032100aUsing Web Text Analytics to Categorize the Business Focus  of Innovative Digital Health Companies0 aUsing Web Text Analytics to Categorize the Business Focus of Inn  aOttawabTalent First Networkc10/2021  a65-780 v113 aCategorizing 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.10aDigital health sector10amachine learning10aMarket offer10atopic modeling algorithm10avalue proposition10aweb analytics1 aAweisi, Abdulla1 aArora, Daman1 aEmby, Renée1 aRehman, Madiha1 aTanev, George1 aTanev, Stoyan  utimreview.ca/article/145702261nas a2200265   4500008004100000022001400041245012800055210006900183260004200252300001000294490000600304520139900310653001401709653002801723653002601751653002101777653002401798653003001822653002901852100001801881700001801899700002301917700001801940856003701958       2017                        eng d  a1927-032100aUsing Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors0 aUsing Artificial Intelligence and Web Media Data to Evaluate the  aOttawabTalent First Networkc06/2017  a25-370 v73 aIn this article, we describe our efforts to adapt and validate a web search and analytics tool – the Gnowit Cognitive Insight Engine – 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’ 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’ competitive actions. In our findings, we see that our methodology reveals a moderate degree of correlation between the Insight Engine’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.10aanalytics10aartificial intelligence10abusiness intelligence10aentrepreneurship10aonline textual data10aprecision medicine sector10astartup growth potential1 aDroll, Andrew1 aKhan, Shahzad1 aEkhlas, Ehsanullah1 aTanev, Stoyan  uhttp://timreview.ca/article/1082