US 12,236,451 B2
Method and system of engaging a transitory sentiment community
Vaibhav Bhan, Toronto (CA); and Joe Lai, Toronto (CA)
Assigned to HIWAVE TECHNOLOGIES INC., Toronto (CA)
Filed by HIWAVE TECHNOLOGIES INC., Toronto (CA)
Filed on Feb. 9, 2023, as Appl. No. 18/107,714.
Application 18/107,714 is a continuation in part of application No. 17/204,324, filed on Mar. 17, 2021, granted, now 11,605,004.
Application 17/204,324 is a continuation in part of application No. 16/216,038, filed on Dec. 11, 2018, granted, now 11,030,533, issued on Jun. 8, 2021.
Prior Publication US 2023/0267502 A1, Aug. 24, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 17/30 (2006.01); G06Q 30/0251 (2023.01)
CPC G06Q 30/0251 (2013.01) 14 Claims
OG exemplary drawing
 
1. A method, performed in a processor of a server computing device, of engaging a transitory sentiment community, the method comprising:
identifying, responsive to monitoring generation of the transitory sentiment community, a critical engagement juncture being reached, the transitory sentiment community including a collective of content consumers, ones of the collective of content consumers providing a sentiment expressive usage associated with a subject of interest, the sentiment expressive usage characterized in accordance with a sentiment intensity rating; and
initiating, responsive to the critical engagement juncture being reached, an engagement action directed to at least a subset of the ones of the collective of content consumers;
wherein the sentiment intensity rating is determined based on with a sentiment analysis performed in accordance with a trained a machine learning model in conjunction with social media content, the trained machine learning model being trained in accordance with a set of input layers interconnected via a set of intermediate layers to an output layer, the machine learning model being instantiated in the processor based at least in part upon processor-executable instructions accessed by the processor from a non-transitory memory, and wherein training the trained machine learning model comprises providing social media content relating to the subject of interest to the set of input layers of the machine learning model, the set of intermediate layers configured in accordance with an initial matrix of weights and being based at least in part upon recursively adjusting the initial matrix of weights by backpropagation in diminishment of an error matrix computed at the output layer.