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Aspect-level sentiment analysis method and device and computer readable storage medium

A sentiment analysis and aspect technology, applied in computer components, computing, neural learning methods, etc., can solve problems such as numerous parameters, time-consuming training process, complex network structure, etc., achieve low training cost, efficient and accurate processing, and network The effect of simple structure

Pending Publication Date: 2021-10-01
CHINA UNIONPAY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The network structure is complex, there are many parameters, and the training process is time-consuming

Method used

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  • Aspect-level sentiment analysis method and device and computer readable storage medium
  • Aspect-level sentiment analysis method and device and computer readable storage medium
  • Aspect-level sentiment analysis method and device and computer readable storage medium

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Embodiment Construction

[0029] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0030] In this application, it should be understood that terms such as "comprising" or "having" are intended to indicate the presence of features, numbers, steps, acts, components, parts or combinations thereof disclosed in this specification, and do not exclude one or more the possibility of the existence of any other feature, number, step, act, component, part, or combination thereof.

[0031] In addition, it should be noted that...

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Abstract

The invention provides an aspect-level sentiment analysis method and device and a computer readable storage medium, and the method comprises the steps: providing a target text to an input layer of a convolutional neural network, and obtaining a corresponding input feature matrix; providing the input feature matrix to a first convolutional layer to obtain a corresponding first feature map, and processing the first feature map by using a first activation function to obtain a first feature vector, wherein the first feature map reflects emotional feature information of the target text; processing the first feature map according to the aspect feature information of the aspect to be analyzed to obtain a second feature map, and processing the second feature map by adopting a second activation function to obtain a second feature vector, wherein the second feature map reflects association information of the target text and the aspect to be analyzed; and judging the emotional polarity of the target text corresponding to the to-be-analyzed aspect according to the first feature vector and the second feature vector. The method enables the model to be simple in structure and low in operand.

Description

technical field [0001] The present application belongs to the field of text sentiment analysis, and in particular relates to an aspect-level sentiment analysis method, device and computer-readable storage medium. Background technique [0002] This section is intended to provide a background or context to the implementations of the application that are recited in the claims. The description herein is not an admission that it is prior art by inclusion in this section. [0003] Aspect-level sentiment analysis is to analyze the sentiment polarity of multiple aspects of a piece of text. Emotional polarity generally includes positive (also known as positive), negative (also known as negative), and neutral. [0004] In the related technology, a convolutional neural network (CNN) with a long short-term memory (LSTM) combined with an attention mechanism is used for aspect-level sentiment analysis. The network structure is complex, there are many parameters, and the training proces...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/33G06F16/35G06K9/62G06N3/04G06N3/08
CPCG06F16/3344G06F16/35G06N3/08G06N3/045G06F18/2411
Inventor 王阳邱雪涛王宇
Owner CHINA UNIONPAY
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