US 12,475,407 B2
Predicting topic sentiment using a machine learning model trained with observations in which the topics are masked
Guilford T. Parsons, Seattle, WA (US); and Kaitlin Claveau, Westminster, CO (US)
Assigned to Providence St. Joseph Health, Renton, WA (US)
Filed by Providence St. Joseph Health, Seattle, WA (US)
Filed on Oct. 27, 2020, as Appl. No. 17/081,277.
Prior Publication US 2022/0129784 A1, Apr. 28, 2022
Int. Cl. G06N 20/00 (2019.01); G06F 40/40 (2020.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01)
CPC G06N 20/00 (2019.01) [G06F 40/40 (2020.01); G16H 10/20 (2018.01); G16H 50/20 (2018.01)] 11 Claims
OG exemplary drawing
 
8. A system comprising:
at least one processor; and
at least one memory, the at least one memory not constituting a transitory propagating data signal, wherein the at least one memory is configured to cause the at least one processor to:
receive a plurality of training natural-language text strings;
identify one or more noun phrases in each of the plurality of training natural-language text strings;
determine one or more sentiment-qualified topics based on the one or more noun phrases;
determine an entity class for each of the one or more sentiment-qualified topics:
modify each training natural-language text string to preserve a location of each sentiment-qualified topic in each training natural-language text string while replacing all information about an identity of each sentiment-qualified topic;
use the modified plurality of training natural-language strings and the entity class for each of the one or more sentiment-qualified topics to train a machine learning model to determine a sentiment of each sentiment-qualified topic by at least performing a largest-first comparison of substrings of each of the plurality of training natural-language text strings to identify longer, multi-word topics to an exclusion of included shorter topics, and comparing the longer, multi-word topics to a list of topics or named entities specified for a domain; and
store the sentiment with each training natural-language text string by restoring the identity and using the location associated with each sentiment-qualified topic in each training natural-language text string.