武永亮,赵书良,李长镜,魏娜娣,王子晏. 基于TF-IDF和余弦相似度的文本分类方法[J]. 中文信息学报, 2017, 31(5): 138-145.
WU Yongliang, ZHAO Shuliang, LI Changjing, WEI Nadi, WANG Ziyan. Text Classification Method Based on TF-IDF and Cosine Similarity. , 2017, 31(5): 138-145.
Text Classification Method Based on TF-IDF and Cosine Similarity
WU Yongliang1, 2, ZHAO Shuliang1, 2, LI Changjing1, 2, WEI Nadi3, WANG Ziyan4
1.College of Mathematics and Information Science, HeBei Normal University, Shijiazhuang, Hebei 050024, China;
2.Hebei Key Laboratory of Computational Mathematics and Applications, Shijiazhuang, Hebei 050024, China;
3.Huihua College of Hebei Normal University, Shijiazhuang, Hebei 050091, China;
4. College of Computer Science and Technology, University of Science&Technology China, Hefei, Anhui 230022, China
Abstract：Text classification is the fundamental task for text mining. Many text classification algorithms have been presented in previous literatures, such as KNN, Nave Bayes, Support Vector Machine, and some improved algorithms. The performance of these algorithms depends on the data set and does not have self-learning function. This paper proposes an effective approach for text classification. The three key points of the approach are: 1)extracting the keywords of category (KWC) of labeled texts based on the TF-IDF approach, 2) classifying unlabeled text by the relevancy of category and unlabeled text, and 3) improving the performance of the approach via updating the KWC in the process of classification. Simulation experiment results show that the new approach can improve the accuracy of text classification to 90%, and even up to 95% when the data volume is large enough. The method can automatically update the keywords of category to improve the classification accuracy of the classifier.
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