A classification method of public opinion hot spots based on deep learning
A technology of deep learning and category classification, applied in the field of deep learning and natural language processing, can solve the problems of insufficient classification function and low accuracy rate, and achieve accurate classification of multiple categories of public opinion hotspots, less time spent, high accuracy and multiple classifications Effect
Active Publication Date: 2020-06-09
CHINA UNIV OF MINING & TECH
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[0008] Purpose of the invention: The present invention aims at text data derived from public opinion hotspot events, in order to solve the problem of insufficient classification function and low accuracy in the prior art, and proposes a method for classifying public opinion hotspots based on deep learning
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[0043] The present invention will be further described below in conjunction with the accompanying drawings.
[0044] The entire process of the present invention is divided into two stages, the model training stage in the early stage and the prediction and classification stage in the later stage. These two stages are similar to the preprocessing process of the data set and the probabilistic topic representation process, that is to say, the text representation we read into the neural network is the same in the two stages, the difference is that the text in the early stage is used for model training, and the text in the later stage is used for training the model. The text of is used to make result classification predictions.
[0045] The technical solution of the present invention will be further described below according to the accompanying drawings.
[0046] figure 1 It is a flowchart of the implementation of the present invention, which can be divided into two stages in the ...
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A deep-learning-based public opinion hotspot category classification method, comprising: collecting and pre-processing a training data set, establishing a probability topic presentation model, carrying out two probability distribution presentations, i.e. document-topic and topic-vocabulary, on a text data set, and inputting an obtained topic-vocabulary matrix into a pre-established neural network model for training so as to learn text features; and a network output layer selecting Softmax to carry out normalization processing and classification prediction. The method solves the dimensionality reduction problem of long text public opinion hotspot data, and realises the automatic extraction of deep features of public opinion hotspot information, so that the classification of multiple categories of public opinion hotspots is more accurate.
Description
technical field [0001] The invention relates to the technical field of deep learning and natural language processing, in particular to a method for classifying public opinion hotspots based on deep learning. The method is a Chinese text classification method specifically applied to the scientific classification of public opinion hotspots for public opinion analysis. Background technique [0002] In today's era of highly developed information technology, after a certain event in society, the general public can quickly learn the ins and outs of the matter through various channels, and a large number of comments will follow. This is public opinion, and the analysis of public opinion plays a crucial role in how to deal with the incident in the future. Before analyzing a hotspot of public opinion, it should be defined scientifically. For example, hotspot events can generally be divided into sudden natural disasters, production safety accidents, mass incidents, public health inci...
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IPC IPC(8): G06F16/35G06N3/08
CPCG06F16/355G06N3/08
Inventor 周勇刘兵刘敬学王重秋
Owner CHINA UNIV OF MINING & TECH