Abstract
In micro-blog short text clustering, the amount of text information contained in micro-blog short text is small, with timeliness, sparseness and singularity, so the artificial selection of attribute words has some limitations. This paper puts forward the feature extraction of text information in micro-blog using Bootstrapping algorithm, it can choose the higher theme information reflect the characteristics of words, and then use the improved TFIDF algorithm to calculate the weight of micro-blog based text clustering. Finally, K-means clustering algorithm is used to cluster micro-blog short text. The experimental results show that the clustering algorithm of micro-blog short text proposed in this paper is better than other algorithms, which improves the clustering effect of micro-blog short text.
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