Presenting a Thematic Model of Health Scientific Productions Using Text-Mining Methods

With the proliferation of the Internet and the rapid growth of electronic articles, text categorization has become one of the key and important tools for data organization and management. In the text categorization, a set of basic knowledge is provided to the system by learning from this set, the new input documents into one of the subject groups. In health literatures due to the wide variety of topics, preparing such a set of early education is a very time consuming and costly task. The purpose of this article is to present a hybrid model of learning (supervised and unsupervised) for the subject classification of health scientific products that performs the classification operation without the need for an initial labeled set. To extract the thematic model of health science texts from 2009 to 2019 at PubMed database, data mining and text mining were performed using machine learning. Based on Latent Dirichlet Allocation model, the data were analyzed and then the Support Vector Machine was used to classify the texts. In the findings of this study, model was introduced in three main steps. In the first step, the necessary preprocessing was done on the dataset due to the elimination of unnecessary and unnecessary words from the dataset and increasing the accuracy of the proposed model. In the second step, the themes in the texts were extracted using the Latent Dirichlet Allocation method, and as a basic training set in step 3, the data were backed up by the Support Vector Machine algorithm and the classifier learning was performed with the help of these topics. Finally, with the help of the categorization, the subject of each document was identified. The results showed that the proposed model can build a better classification by combining unsupervised clustering properties and prior knowledge of the samples. Clustering on labeled samples with a specific similarity criterion merges related texts with prior knowledge, then the learning algorithm teaches classification by supervisory method. Combining categorization and clustering can increase the accuracy of categorization of health texts.