BTM and GloVe Similarity Linear Fusion-Based Short Text Clustering Algorithm for Microblog Hot Topic Discovery

Abstract

Microblog hot topic discovery is one of the research hotspots in the field of text mining. The distance function of traditional K-means leads to low clustering accuracy, which leads to poor hot topic discovery. Three definitions are proposed in this paper: title words and body words, positional contribution-based weight and fusion similarity-based distance. The short text clustering algorithm based on BTM and GloVe similarity linear fusion (BG SLF-Kmeans) is further proposed. BTM and GloVe are used to model the preprocessed microblog short texts. JS divergence is adopted to calculate the text similarity based on BTM topic modeling. WMD of improved word weight (IWMD) is used to calculate the text similarity based on GloVe word vector modeling. Finally, the two similarities are linearly fused and used as the distance function to realize K-means clustering. Specific word sets of 6 hot topics can be obtained, and microblog hot topics can be discovered. The experimental results show that BG SLF-Kmeans significantly improves clustering accuracy compared with TF-IDF K-means, BTM K-means, and BTF SLF-Kmeans.