Topic Discovery Method of Stock Bar Forum Based on Integration of Frequent Item-set and Latent Semantic Analysis

Abstract:

To achieve more effective topic discovery of stock bar forum, this paper presents a framework with short text clustering based on frequent itemset and latent semantic (STC_FL). The important frequent itemsets are acquired with the concept vector space based on HowNet, and then a combination pattern of statistics and latent semantics is used to realize the selfadaptive clustering of important frequent itemsets. Finally, the algorithm of text soft classifying based on similarity threshold and nonoverlapping (TSCSN) is proposed. Text soft clustering is selected and controlled with parameter optimization. By taking the real stock bar forum data as a specific case of empirical analysis, it is shown that STC_FL framework and TSCSN algorithm can fully exploit the latent semantic information of text and reduce the dimension of feature space, which realizes the deep information mining and topic classification of short texts.