Research paper classification using text mining approaches

With the exponential growth of digital text data, the need for effective ways to manage and organize them electronically has made natural language processing technique and methods more important than ever before. Classification of research articles by text mining methods is an important aspect of scholarly communication as it provides a systematic way to organize and classify research publications according to their topics, methodologies, and other relevant characteristics. Classification of research articles is an important tool for effective access and discovery of research publications, facilitating research synthesis and analysis, promoting interdisciplinary research, and quality assurance. In this thesis, different classification models were used and their performances were compared. A system is proposed to help researchers search for research articles more efficiently. The data set was studied using the R language and the results were shown with an application prepared with R Shiny.