Abstract—In this paper, the effect of machine translators in the textual data classification is examined by using supervised classification methods. The developed system first analyzes and classifies an input text in one language, and then analyzes and classifies the same text in another language generated by machine translators from the input text. The obtained results are compared to measure the effect of the translators in textual data classification. The performances of the classification method used in this study are also measured and compared.The classification process can be described as training data preparation, feature selection, and classification of the input texts with/without translation. The obtained results show that Multinomial Naïve Bayes method is the most successful method, and that the translation has quite a small effect on the attained classification accuracy.

Index Terms—Text classification, machine translator, translated texts, textual data mining.

The authors are with the Department of Computer Engineering, Facultyof Engineering, Karadeniz Technical University, and 61080 Trabzon,TURKEY.

Cite: Leila Rouka, Cemal Köse, and Parham Tofighi, "Using Machine Translators in Textual Data Classification," International Journal of Computer and Communication Engineering vol. 1, no. 1, pp. 43-46 , 2012.