Emotion prediction method and system based on interactive double-graph convolutional network

Abstract

The invention provides an emotion prediction method and system based on an interactive double-graph convolutional network, and the method comprises the steps: obtaining a to-be-predicted text, and converting the to-be-predicted text into a plurality of word vector sequences; extracting context information of the plurality of word vector sequences to obtain hidden state vectors; based on the plurality of word vector sequences, adopting a syntactic graph convolutional network to obtain a syntactic graph; based on the hidden state vector, adopting a semantic graph convolutional network to obtain a semantic graph; based on the syntactic graph and the semantic graph, the syntactic information and the semantic information are interactively learned, and the syntactic graph and the semantic graph after interaction learning are obtained; and on the basis of the syntactic graph and the semantic graph after interactive learning, performing prediction to obtain emotion probability distribution of the to-be-predicted text. And the accuracy and the stability of emotion polarity judgment are improved.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to an emotion prediction method and system based on an interactive dual graph convolution network. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the rapid development of social media, the number of online comments has also exploded. More and more people are willing to express their attitudes and emotions on the Internet instead of simply browsing and accepting them. A large amount of online comment data is often accompanied by Emotional information of commenters, such as "happy", "angry", "frustrated", etc. Since the user review data appearing on the Internet holds the user's emotional information, individual consumers and enterprises have a positive attitude to whether online reviews have an important influ...

AI Technical Summary

Problems solved by technology

[0005] With the introduction of aspect-level sentiment analysis into the graph neural network model, good results have been achieved, which largely make up for the shortcomings of traditional methods. However, there are still some shortcomings in these studies using graph neural networks, and there are a large number of reviews Problems with syntax errors, informal expressions, and the complexity of online comments

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