US 11,853,704 B2
Classification model training method, classification method, device, and medium
Haisong Zhang, Shenzhen (CN); and Yan Song, Shenzhen (CN)
Assigned to TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED, Shenzhen (CN)
Filed by Tencent Technology (Shenzhen) Company Limited, Shenzhen (CN)
Filed on Mar. 26, 2021, as Appl. No. 17/214,665.
Application 17/214,665 is a continuation of application No. PCT/CN2019/123496, filed on Dec. 6, 2019.
Claims priority of application No. 201811554820.X (CN), filed on Dec. 18, 2018.
Prior Publication US 2021/0216723 A1, Jul. 15, 2021
Int. Cl. G06F 40/30 (2020.01); G06F 18/241 (2023.01)
CPC G06F 40/30 (2020.01) [G06F 18/241 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A method performed at a computing device having one or more processors and memory storing a plurality of computer-readable instructions to be executed by the one or more processors, the method comprising:
training an initial classification model by using a first sample set, to obtain a pre-trained model, the first sample set comprising a quantity of weakly supervised first samples, each first sample comprising a first social text and an emoticon label in the first social text;
training the pre-trained model by using a second sample set, to obtain a social text sentiment classification model, the second sample set comprising a quantity of supervised second samples, each second sample comprising a second social text and a manually-added sentiment classification label corresponding to the second social text; and
applying a target social text to the social text sentiment classification model as an input to obtain a sentiment class probability distribution corresponding to the target social text as an output.