Research and Application of LDA-Based Topic Modelling and Co-Occurrence Semantic Web Analysis

This article focuses on two important text analysis methods, LDA topic modelling and co-occurrence semantic network analysis. Firstly, the article introduces how the LDA topic model, as an unsupervised learning tool, automatically extracts the topic structure from a large amount of unlabelled text by simulating the document-topic and topic-vocabulary generating process, providing effective support for tasks such as text categorization, information retrieval, and knowledge graph construction. Secondly, the co-occurrence semantic network analysis method is analysed in detail, which constructs a network model based on lexical co-occurrence relations, and measures the strength of association between nodes by using statistical indexes in order to reveal the semantic links between words, and then applies to the fields of lexical-semantic relation visualization, word sense disambiguation, and knowledge graph construction. Finally, the latest research progress, application scenarios and future development possibilities of the two are discussed in depth to provide comprehensive theoretical guidance and practical insights for academic research and practical applications.