A Method for Analyzing the Sentiment Polarity of Long Text News Public Opinion

Abstract

The invention discloses a method for analyzing the emotional polarity of long-text news public opinion, comprising the following steps: S1 collects text data as training samples, S2 performs data cleaning on the data of the training samples collected in step S1, and cleans the Process the special characters in the data of the training sample to obtain the data set; S3 split the data set, divide the training sample into a training set and a test set according to the proportion; S4 build a deep learning network based on the representation model and load the pre-training parameters; S5 builds a long text emotional polarity analysis network model; S6 modifies the training sample data structure; S7 model training, adopts the method of stratified sampling and K-fold cross-validation, and guarantees that the proportion of samples in the sample data set of each fold is the same as that in stratified sampling. The proportion of the original data is the same, and the results of each fold model are saved in the model with the highest score in the verification set. The comprehensive K-fold model is tested on the test set, and the average probability is taken as the test result of the model.

Description

technical field [0001] The invention belongs to the technical field of natural language processing, and in particular relates to a method for analyzing the emotional polarity of long-text news public opinion based on a Transformer structure. Background technique [0002] Judging the emotional polarity of news public opinion can be abstracted as a text classification problem in the field of natural language processing, that is, to judge the emotional polarity expressed by the news through the title and text. At present, the methods used for sentiment analysis are mainly divided into the following three types: [0003] 1. Method based on sentiment lexicon. The process of the traditional model method based on the emotional dictionary is to first construct the emotional dictionary, and use the dictionary to judge the emotional orientation and emotional strength of the words in the pre-analysis text, so as to realize the overall emotional classification of the text. Limitations...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a method for analyzing the emotional polarity of long-text news public opinion, which encodes the long text through a transformer (Transformer), and then analyzes the long text through a bidirectional threshold recurrent unit network (Bi-GRU). The method of text news public opinion sentiment polarity can solve the problem of low accuracy of traditional methods

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