DEEP-LEARNING-BASED PUBLIC OPINION HOTSPOT CATEGORY CLASSIFICATION METHOD

摘要

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.