US 12,001,789 B2
Textual data analytics
Fanxi Zhao, Boston, MA (US); David Pope, North Andover, MA (US); Daniel Sandberg, North Babylon, NY (US); Temilade Oyeniyi, Chicago, IL (US); Ashish Sumsher Rana, Silver Springs, MD (US); and Eric Oak, Framingham, MA (US)
Assigned to S&P Global Inc., New York, NY (US)
Filed by S&P Global Inc., New York, NY (US)
Filed on Aug. 27, 2021, as Appl. No. 17/446,220.
Prior Publication US 2023/0061590 A1, Mar. 2, 2023
Int. Cl. G06F 40/20 (2020.01); G06F 40/216 (2020.01); G06F 40/279 (2020.01); G06N 5/04 (2023.01)
CPC G06F 40/216 (2020.01) [G06F 40/279 (2020.01); G06N 5/04 (2013.01)] 36 Claims
OG exemplary drawing
 
1. A computer-implemented method of textual data analysis, the method comprising:
using a number of processors to perform the steps of:
parsing, according to natural language processing, text data extracted from a first number of transcripts related to a number of companies;
creating a number of intermediate metrics comprising numerical representations of the parsed text data and derivations of the parsed text data;
combining the intermediate metrics into a number of different combinations, wherein each combination comprises a headline analytic;
testing, according to a machine learning model, each headline analytic for standalone predictive efficacy based on historical data;
selecting headline analytics with standalone predictive efficacies above a first threshold;
testing, according to the machine learning model, each selected headline analytic for additive predictive efficacy to determine if the selected headline analytic incrementally increases the predictive efficacy of a preexisting economic analytic above a second threshold based on historical data; and
applying, to a second number of transcripts, the selected headline analytics with an additive predictive efficacy above the second threshold, in combination with the preexisting economic analytic, to predict financial performance of companies that are the subjects of the second number of transcripts.