Open AccessResearch on the Prediction of Popular Opinion Trend of Web News based on BP neural Network and LSTM
Author(s) -
Jiale Wu,
Lulin Zhang,
Fulian Yin
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/646/1/012003
Subject(s) - immediacy , word2vec , computer science , public opinion , artificial neural network , sentiment analysis , artificial intelligence , machine learning , mean squared error , deep learning , statistics , mathematics , political science , philosophy , embedding , epistemology , politics , law
Current prediction methods on public opinion trend of network news are often carried out according to human experience or traditional time series prediction, but they sometimes lack scientific principle and immediacy. This paper researches on the prediction of public opinion trend of network news based on deep learning. Firstly, we use the BP neural network and LSTM to predict public sentiment trend, then combine word2vec with LSTM to classify the text sentiment and finally use doc2vec algorithm and k-means to cluster the text. Experiment result shows that the mean square error with good performance, which reveals the beneficial effect of our prediction method.