Semantic Data Mining for Reading Promotion News Based on LDA Topic Models

Abstract

In this paper, we take the reading promotion articles published by news papers as research samples, and use python coding to implement LDA theme modeling analysis and semantic data mining of reading promotion articles, aiming to analyse the theme characteristics and
development strategies of reading promotion in China. The study found that the topics of reading promotion reported by Chinese newspapers
and magazines can be classified into four categories according to the targets of reading promotion: reading promotion in urban bookstores,
reading promotion in red history, reading promotion in rural bookstores, and reading promotion in parent-child activities, and analysed the
characteristics and development direction of each category. In order to carry out personalised reading promotion activities according to different targets, provide the public with more accurate and targeted services, achieve better reading promotion effects, and provide reference value
for the future development of reading promotion in China.