Designing a Predictive Analytics Solution for Evaluating the Scientific Trends in Information Systems Domain

Full-text articles are mostly provided in electronic formats, but it still remains a challenge to find deeply related works beyond the keywords and abstract contexts. Identification of related scientific articles based on keywords and abstracts help researchers to find desired and informative content, but it requires the utilization of variety of text analytics (mining) methods to address the need. This study investigates information systems (IS) articles to cluster and evaluate the domain of knowledge of recent Information Systems publications using text clustering methods, and then to predict the exact clusters of knowledge of new articles using classification methods. This categorization and predictive learning approach help the scholars and practitioners to find the most relevant articles for their researches and practical endeavors through an automated mechanism. Articles have been collected from the Scopus repository. The dataset has been narrowed to specific areas of recent information