A New Model for Automatic Text Classification

Hekmatullah Mumivand (Software Engineering Department, Lorestan University, Aleshtar Higher Education Center, KhorramAbad, Lorestan,IR Iran)
Rasool Seidi Piri (Software Engineering Department, Lorestan University, Aleshtar Higher Education Center, KhorramAbad, Lorestan,IR Iran)
Fatemeh Kheiraei (Engineering Department, Lorestan University, KhorramAbad, Lorestan, IR Iran)

Abstract


In this paper,a new method for automatic classification of texts is presented.This system includes two phases;text processing and text categorization.In the first phase,various indexing criteria such as bigram,trigram and quad-gram are presented to extract the properties.Then,in the second phase,the W-SMO machine learning algorithm is used to train the system.In order to evaluate and compare the results of the two criteria of accuracy and readability,Macro-F1 and Micro-F1 have been calculated for different indexing methods. The results of experiments performed on 7676 standard text documents of Reuters showed that the best performance is related to w-smo bigram criteria with accuracy of 95.17 micro and 79.86 macro.Also,the results indicated that our proposed method has the best performance compared to the W-j48,Naïve Bayes,K-NN and Decision Tree algorithms.

Keywords


Text classification;Machine learning;W-SMO;N-gram

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DOI: https://doi.org/10.30564/ese.v3i1.3170

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