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Chinese-Text-Classification-Based-on-Naive-Bayes
The development of computer and communications technology has resulted in huge amount of data. The automatic text classification technique has become very significant. Naive Bayes algorithm is based on probabilistic model. It is an effective way to deal with automatic text classification. The main task of this paper is to discuss the theoretical basis of Naive Bayes text classifier and describe the process of using Java language to accomplish the classifier. We can divide the classifier into two parts: the feature extraction and the calculation according to the feature. In the feature extraction part, I use the Chinese word segmentation method and the stop words filtering. In the classification part, I calculate the prior probability, the likelihood function value and the maximum a posterior estimation. During the simple test, the author uses the Sogou laboratory’s text classification corpus as the training set and the test set. During the test, the accuracy is between 39% to 56 %. The results show that there is still room for improvement. The paper also includes the discussion of its improvement methods and wider application.
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chenergy1991's Repositories
chenergy1991/Chinese-Text-Classification-Based-on-Naive-Bayes
The development of computer and communications technology has resulted in huge amount of data. The automatic text classification technique has become very significant. Naive Bayes algorithm is based on probabilistic model. It is an effective way to deal with automatic text classification. The main task of this paper is to discuss the theoretical basis of Naive Bayes text classifier and describe the process of using Java language to accomplish the classifier. We can divide the classifier into two parts: the feature extraction and the calculation according to the feature. In the feature extraction part, I use the Chinese word segmentation method and the stop words filtering. In the classification part, I calculate the prior probability, the likelihood function value and the maximum a posterior estimation. During the simple test, the author uses the Sogou laboratory’s text classification corpus as the training set and the test set. During the test, the accuracy is between 39% to 56 %. The results show that there is still room for improvement. The paper also includes the discussion of its improvement methods and wider application.
chenergy1991/Youku-Android-APP-Sniffer
chenergy1991/BirthdayCard
chenergy1991/chenergy1991.github.io
My blog~~
chenergy1991/DetectionScript
chenergy1991/docker-network-graph
Quickly visualize docker networks with graphviz.
chenergy1991/docs
documents worth spreading
chenergy1991/P_QQ_Management
chenergy1991/SDPcontroller
Control Module for Software Defined Perimeter (SDP)
chenergy1991/WebSecurityTestcases