Text stance detection method based on text and user representation learning

A user and text technology, applied in neural learning methods, biological neural network models, semantic analysis, etc., can solve problems such as the inability to achieve effective mixing of semantics between modalities, and achieve the effect of improving efficiency and accuracy, and strong feature expression ability

Active Publication Date: 2022-03-25
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

Different modals have different semantics, and simple splicing cannot achieve effective mixing of semantics between modals

Method used

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  • Text stance detection method based on text and user representation learning
  • Text stance detection method based on text and user representation learning
  • Text stance detection method based on text and user representation learning

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Embodiment

[0031] figure 1 It is a flow chart of a specific embodiment of the text position detection method based on text and user representation learning in the present invention. Such as figure 1 As shown, the specific steps of the text position detection method based on text and user representation learning of the present invention include:

[0032] S101: text data preprocessing:

[0033] Determine the social media platform that needs text position detection, and collect the text data set of the topic that needs text position detection from the social media platform. The text data set includes several texts related to the topic, and the users who publish these texts Watchlist and Followed list.

[0034] The behavior of following on social media platforms expresses the one-way interest between users, and this one-way interest is often generated due to similar interests and similar views. Intuitively, the closer two people follow the list, the more The user feature representation i...

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Abstract

The invention discloses a text stance detection method based on text and user representation learning, which acquires a text data set from a social media platform, generates a user social relationship graph and obtains a corresponding Laplacian matrix, and determines the stance of each text Label vector, use the pre-trained BERT model to obtain the text vector of each text, build and train the position detection model, when it is necessary to detect the position of the user text, generate the user social relationship graph and obtain the corresponding Laplacian matrix, the The text vector is input into the position detection model to obtain the position detection result. The present invention respectively acquires two modal features of text and user and performs cross-modal fusion, so as to realize accurate text position detection.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and more specifically relates to a text position detection method based on text and user expression learning. Background technique [0002] Stance detection is one of the cutting-edge research branches in the field of Natural Language Processing (NLP). Its purpose is to automatically detect people's views or attitudes on individuals, things, and events from text information, such as "support , opposition or neutrality". [0003] At present, the existing position detection methods mainly adopt classical models mainly based on CNN (Convolutional Neural Networks, Convolutional Neural Networks) and RNN (Recurrent Neural Networks, Recurrent Neural Networks) models. Most of the existing stance detection methods only use information in the text dimension for stance detection, but do not take advantage of user features that are highly related to stance. The user's own attributes and...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F40/30G06N3/04G06N3/08
CPCG06F40/30G06N3/084G06N3/047G06N3/045G06N3/044
Inventor 彭愈翔罗绪成
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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