Coarse-grained emotion analysis method based on hierarchical BERT neural network

A technology of sentiment analysis and neural network, which is applied in the field of Web mining and intelligent information processing, can solve problems such as difficult in-depth description of text semantic information, lack of classification data and unbalanced problem optimization processing, etc., to achieve accuracy optimization and improve accuracy , the effect of broad application prospects

Active Publication Date: 2019-08-20
BEIJING INSTITUTE OF TECHNOLOGYGY
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AI Technical Summary

Problems solved by technology

Traditional language models are mostly shallow one-way neural networks, which are difficult to deeply describe or reflect the semantic information of the text
Second, the existing coarse-grained sentiment analysis methods lack deep mining of text semantic information when generating text vector representations, and lack of optimal processing for the imbalanced problem of classified data categories

Method used

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  • Coarse-grained emotion analysis method based on hierarchical BERT neural network
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  • Coarse-grained emotion analysis method based on hierarchical BERT neural network

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Embodiment 1

[0033] Based on the coarse-grained sentiment analysis method of the present invention, the Ubuntu 16.04 operating system is used as the development platform, the Debian Almquist Shell (dash) and python2.7 are selected as the development language, and the deep learning development framework is TensorFlow 1.9. Such as figure 1 Shown is a coarse-grained sentiment analysis method based on a hierarchical BERT neural network, which includes the following steps:

[0034] Step 1. Obtain the corpus for coarse-grained sentiment analysis

[0035] Preferably, the acquisition of the corpus is completed through the following process: collect the text information of the Internet review website, and construct it as a corpus for coarse-grained sentiment analysis; or collect an existing comment text data set constructed according to the comment website information, as a corpus for coarse-grained sentiment analysis . You can collect data from the review website Yelp. Yelp includes user reviews...

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Abstract

The invention relates to a coarse-grained emotion analysis method based on a hierarchical BERT neural network, and belongs to the technical field of Web mining and intelligent information processing.The method comprises the following steps: corpus acquisition: acquiring a corpus of coarse-grained sentiment analysis; corpus preprocessing, wherein character cleaning, subordinate clause segmentationand subordinate clause vector construction are included; constructing sentence vectors: calculating the subordinate clause vectors by utilizing a bidirectional long and short term memory network, a multi-layer perceptron and an attention mechanism to generate sentence vectors; gradient coordination mechanism optimizing: introducing the gradient coordination mechanism to solve the problem of datatype imbalance in coarse-grained sentiment analysis; and carrying out coarse-grained sentiment analysis by adopting a hierarchical BERT neural network. Compared with the prior art, t the sentence vectors containing deep semantic information are constructed for the comment text through the hierarchical BERT neural network, the accuracy of coarse-grained emotion analysis tasks is improved, and the method has a wide application prospect in the fields of information recommendation, public opinion monitoring and the like.

Description

technical field [0001] The invention belongs to the technical field of Web mining and intelligent information processing, and relates to a coarse-grained sentiment analysis method based on a hierarchical BERT (Bidirectional Encoder Representation from Transformers, full name Bidirectional Encoder Representation from Transformers) neural network. Background technique [0002] Coarse-grained text sentiment mining, referred to as coarse-grained sentiment analysis. Coarse-grained sentiment analysis aims to classify the sentiment polarity of review texts. Sentiment polarity includes three categories: commendatory, derogatory and neutral. Commentary text is divided into chapter-level commentary text and sentence-level commentary text. [0003] At present, coarse-grained sentiment analysis methods mainly include methods based on rule and statistical fusion, methods based on machine learning, and methods based on deep learning. [0004] The coarse-grained sentiment analysis metho...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/35G06N3/04
CPCG06F16/36G06F16/35G06N3/045
Inventor 张春霞孙现超王卓罗妹秋吕光奥
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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