Aspect-Level Sentiment Classification Method Based on BERT and Multilayer Attention Mechanism

A sentiment classification and attention technology, applied in the direction of text database clustering/classification, semantic analysis, instruments, etc., can solve the problems of information loss, gap, complex manual design, etc., to achieve the effect of improving accuracy and precision

Active Publication Date: 2022-06-24
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0007] (1) The method based on traditional machine learning needs to manually design complex features, which is very time-consuming
[0008] (2) Most of the existing deep learning-based methods use RNN to extract semantic information, such as: LSTN, CNN, although they have achieved good results, but compared with BERT to extract semantic information, there is still a certain gap
[0009] (3) The current model using the Attention mechanism is to calculate the attention of the Aspect as a whole to the Context word, without considering the attention of a single word in the Aspect to the Context word, which may easily lead to information loss

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  • Aspect-Level Sentiment Classification Method Based on BERT and Multilayer Attention Mechanism
  • Aspect-Level Sentiment Classification Method Based on BERT and Multilayer Attention Mechanism
  • Aspect-Level Sentiment Classification Method Based on BERT and Multilayer Attention Mechanism

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Embodiment Construction

[0050] The technical solutions of the present invention are further described below with reference to the accompanying drawings.

[0051] like figure 1 As shown, the aspect-level sentiment classification method based on BERT and multi-layer attention mechanism of the present invention includes the following steps:

[0052] S1. Preprocess the training corpus; including the following sub-steps:

[0053] S11. Extract the Aspect word in each comment (a corpus may contain multiple Aspect words) from the training corpus data, and obtain the Aspect word set DataAspect;

[0054] S12. Extract a comment corresponding to each Aspect word from the training corpus data to obtain a set DataContext;

[0055] S13. Count the sentiment polarity of the Aspect word and the corresponding corpus, where 1 represents positive, 0 represents neutral, and -1 represents negative, and the label set LableSet is obtained;

[0056] S14. Perform position symbol processing on the DataContext, and add a pos...

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Abstract

The invention discloses an aspect-level emotion classification method based on BERT and a multi-layer attention mechanism, comprising the following steps: S1, preprocessing the training corpus; S2, establishing an aspect-level emotion classification model based on multi-layer attention; S3, Use the trained classification model to perform sentiment classification on the data to be predicted. The invention uses a multi-layer Attention mechanism to fully tap the correlation between aspcet words and DataContext words, and can effectively improve the accuracy of aspect-level emotion classification.

Description

technical field [0001] The present invention relates to an aspect-level sentiment classification method based on BERT and multi-layer attention mechanism. Background technique [0002] With the rise and development of social networks and e-commerce platforms, people prefer to post their comments online, such as comments on products on JD.com and Taobao, and comments on food on Meituan and Hungry. Merchants have valuable value. A comment may contain evaluations of multiple aspects. How to quickly obtain the sentiment polarity of aspect words has become an important research direction in the field of natural language processing. [0003] At present, there are mainly two processing methods: [0004] 1. Methods based on traditional machine learning, such as support vector machine (SVM) and logistic regression, extract features that depend on corpus implementation, and need to manually design features for data, and the quality of the model depends on the quality of feature desi...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/35G06F40/216G06F40/284G06F40/30G06F40/126G06N3/04
CPCG06F16/355G06F40/216G06F40/284G06F40/30G06F40/126G06N3/045
Inventor 廖伟智黄鹏伟阴艳超
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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