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A clustering method based on the Latent Dirichlet Allocation and the VSM model to compute the text similarity is presented. The Latent Dirichlet Allocation subject models and the VSM vector space model weights strategy are used respectively to calculate the text similarity. The linear combination of the two results is used to get the text similarity. Then the k-means clustering algorithm is chosen for cluster analysis. It can not only solve the deep semantic information leakage problems of traditional text clustering, but also solve the problem of the LDA that could not distinguish the texts because of too much dimension reduction. So the deep semantic information is mined from the text, and the clustering efficiency is improved. Through the comparisons with the traditional methods, the result shows that this algorithm can improve the performance of text clustering.

Cluster Analysis for Internet Public Sentiment in Universities by Combining Methods