Used the idea of cloud computing, according to MapReduce model to solve the traditional Bayesian classification algorithm suited to large-scale data deficiencies, greatly improved the speed of classification. The combination of the characteristics of the parallel algorithm was improved accordingly. Adding synonyms and word frequency filtering combined approach allows vector dimensionality reduction, reducing false positives. Wherein the particular keyword was then weighted to enhance the accuracy of the classification. Finally, the Hadoop cloud computing platform was experimentally proved that the traditional text classification algorithm after parallelization on Hadoop cloud computing platforms, has better speedup, and the improved algorithm can improve the classification accuracy.
Cloud computing, Text classification, Parallel, Hadoop
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