Abstract: According to the field of network public opinion analysis, five clustering algorithms: system clustering, string kernels, K nearest neighbor algorithm, support vector machine algorithm and topic models were studied. A comprehensive comparative research of these five algorithms was conducted by using network public opinion data as data set and R language environment as experimental tool. At the same time, simulation experiments were carried out to compare these five algorithms' strengths and weaknesses. Experimental results show that "topic model" has better applicability than other algorithms in terms of text clustering. After further experiments we also found in topic models, CTM(Correlated Topic Model) method is more suitable for the exploration and discovery of class relations while Gibbs sampling method on the performance of text clustering method is better than the CTM method.

Key words: topic model, network public opinion, text knowledge discovery, text clustering