Content Analysis of Science Paper's by Using Text Mining

In this research, text mining technique has been applied to analyze the content of scientific articles, also to represent a model for classifying the articles (the first class is comprised of the articles observing predetermined criterias, and the second one consists of those inattentive). To meet these objectives, in the current research, the expert's ideas have been used to elicit qualitative criteria, and 15 final criteria's were ultimately picked up. In the next phase, 18 scientific articles in industrial engineering scope were studied by university professors and PHD students (with regard to 15 elicited criteria), and beneficial results were derived. In the end, different classification models were applied to classify the articles and with the aid of some specific techniques, we tried to improve these models. Finally, we compare the models to choose the best one.
The aim of this research is to get some general criteria for analyzing the content of articles, propose different classification models for classifying the scientific articles whit regard to predetermined criterias, and choose the best model. Finally, as a result, 15 final criteria's also different classification models with appropriate accuracy, were ultimately picked up.