Attribute-level text sentiment classification method based on position-gated recurrent neural network

The invention discloses an attribute-level text sentiment classification method based on a position-gated recurrent neural network. The method comprises the following steps: extracting attribute-level text sentiment features based on the position-gated recurrent neural network; and carrying out attr...

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Bibliographic Details
Main Authors BAI QINGCHUN, WANG LAMEI, XIAO JUN
Format Patent
LanguageChinese
English
Published 02.09.2022
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
HANDLING RECORD CARRIERS
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
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Summary:The invention discloses an attribute-level text sentiment classification method based on a position-gated recurrent neural network. The method comprises the following steps: extracting attribute-level text sentiment features based on the position-gated recurrent neural network; and carrying out attribute-level text sentiment classification based on a position-gated recurrent neural network. A text classification reasoning method comprises the following steps: selecting a text sentiment analysis model drive, packaging an original model, realizing automatic analysis of a text, and providing an application execution interface for sentiment analysis operation; the client sends text data to the framework module for extracting and analyzing the emotion features of the text to analyze and process the text sent from the outside, and the analyzed model considers the concurrency of the model, is packaged into a port and is forwarded to the query module; and the server requests semantic analysis and emotion reasoning an
Bibliography:Application Number: CN202210638122