Comparative Analysis of Text Categorizer on Science and Technology Intelligence

Abstract:

In order to more effectively classify the science and technology intelligence text, the idea that classifying science and technology intelligence text categorization based on different classifiers is proposed. The experiment is done with two thousand Chinese texts based on three different classifiers in this paper. Among these classifiers, the rate of correctly classified instances with NaiveBayes Classifier is 96.95 percent and J48 Classifiers is 97.59. The highest of three classifiers is SMO Classifier and its correct rate is 98.65 percent. According to the analysis of experimental results, it is proved that the idea proposed is applicable to science and technology intelligence text categorization and it is able to meet the needs of text categorization.

• Periodical: Applied Mechanics and Materials (Volumes 530-531)
• Pages: 502-505
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.530-531.502
• Online since: February 2014
• Authors: Pei Pei Sun, Quan Yin Zhu, Lei Zhou, Yong Jun Zhang*
• Keywords: Algorithm (GA), Chinese Text Categorization, Classifier, Science and Technology Intelligence

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