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Aspect-level sentiment analysis method and system based on gated cavity convolution and graph convolution

A sentiment analysis and aspect technology, applied in the field of deep learning and natural language processing, can solve the problem of ignoring the grammatical information of text data, achieve the effect of expanding the receptive field and improving performance

Pending Publication Date: 2022-01-07
EPIC HUST TECH WUHAN
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most of the models proposed in the past are based on the attention mechanism and common neural networks, and have made great contributions to how to extract semantic information. However, the grammatical information of text data is ignored, which is also crucial for the discrimination of aspect-level sentiment analysis. of

Method used

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  • Aspect-level sentiment analysis method and system based on gated cavity convolution and graph convolution
  • Aspect-level sentiment analysis method and system based on gated cavity convolution and graph convolution
  • Aspect-level sentiment analysis method and system based on gated cavity convolution and graph convolution

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Embodiment 1

[0032] refer to figure 1 and figure 2 , in the first aspect of the present invention, an aspect-level sentiment analysis method based on gated hole convolution and graph convolution is provided, including: S100. Representing the context and aspect words in the sentence of the text to be analyzed with word vectors respectively , get the embedding of context and aspect words respectively; S200. Input the embedding of the obtained context and aspect words into the gated hole convolutional neural network to obtain the context representation of the integration of aspect information; S300. Incorporate the context of the integration of aspect information Indicates input into the graph convolutional neural network to obtain the predicted emotional polarity of the text to be analyzed.

[0033] It can be understood that the embedding and context representation of the above-mentioned context and aspect words are usually expressed in the form of vectors or matrices, and the aspect words...

Embodiment 2

[0063] refer to Figure 4 , the second aspect of the present invention provides an aspect-level sentiment analysis system 1 based on gated atrous convolution and graph convolution, including:

[0064] The embedding module 11 is used to represent the context and the aspect words in the sentence of the text to be analyzed with word vectors respectively, and obtain the embedding of the context and the aspect words respectively;

[0065] Fusion module 12, input the embedding of the obtained context and aspect words into the gated hole convolutional neural network, and obtain the context representation of the integration aspect information;

[0066] The prediction module 13 inputs the context representation of the integrated aspect information into the graph convolutional neural network to obtain the predicted emotional polarity of the text to be analyzed.

[0067] Further, the embedding module 11 includes a first embedding unit and a second embedding unit, the first embedding uni...

Embodiment 3

[0069] refer to Figure 5 , the third aspect of the present invention provides an electronic device, including: one or more processors; storage means for storing one or more programs, when the one or more programs are used by the one or more executed by one or more processors, so that the one or more processors implement the method provided in the first aspect of the present invention.

[0070] The electronic device 500 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 501, which may be loaded into a random access memory (RAM) 503 according to a program stored in a read-only memory (ROM) 502 or loaded from a storage device 508 Various appropriate actions and processing are performed by the program. In the RAM 503, various programs and data necessary for the operation of the electronic device 500 are also stored. The processing device 501 , ROM 502 and RAM 503 are connected to each other through a bus 504 . An input / output ...

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Abstract

The invention relates to an aspect-level sentiment analysis method and system based on gated cavity convolution and graph convolution, and the method comprises the steps: representing context and aspect words in sentences of a to-be-analyzed text through word vectors, and obtaining the embedding of the context and the embedding of the aspect words; inputting the obtained context and aspect word embedding into a gated dilated convolutional neural network to obtain context representation integrated with aspect information; and inputting the context representation integrated with the aspect information into a graph convolutional neural network to obtain the predicted sentiment polarity of the text to be analyzed. According to the method, the gated cavity convolutional network and the graph convolutional neural network are combined, the text language information is effectively utilized, meanwhile, the importance of aspect information in aspect-level sentiment analysis is emphasized, and the accuracy of aspect-level sentiment analysis tasks is improved.

Description

technical field [0001] The invention belongs to the technical field of deep learning and natural language processing, and in particular relates to an aspect-level sentiment analysis method and system based on gated atrous convolution and graph convolution. Background technique [0002] With the increasing requirements of users for text sentiment analysis, sentiment analysis is changing from a coarse-grained level to a fine-grained level. Aspect-Based Sentiment Analysis (ABSA) aims to identify the emotional category of each specific aspect in a given text. As one of the important subtasks of sentiment analysis, it can dig out more specific and deeper aspects of users for different aspects. It has become an important basis for decision-making in many fields, has great application value, and has evolved into an emerging research direction for natural language processing tasks. [0003] In early studies, aspect-level sentiment analysis was regarded as a general sentiment analys...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F40/211G06F40/284G06N3/04G06N3/08
CPCG06F40/211G06F40/284G06N3/08G06N3/045
Inventor 路松峰杜俊志方波吴俊军姜鹭周军龙周力易王画
Owner EPIC HUST TECH WUHAN
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