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When using Naive Bayes theory as a text classification algorithm, it is especially important to choose an effective feature selection method, due to the hypothesis that occurrence probabilities of features are independent of each other which is equally important. In this paper, jieba Chinese segmentation module’s TF-IDF standard is used to select the features for the training news text and Naive Bayes text classification model is implemented with high performance. Before the test of classification model, it’s still necessary to use the TF-IDF standard to select the keywords for testing news texts. The experiment result showed that this method is of high efficiency in classification.

The Simply Implement of Effective Naive Bayes Web News Text Classification Model