Research Trend Analysis using Word Similarities and Clusters

In this paper, we propose a new research trend analysis using important word clusters and its relationship. Journals published many papers every month or week and new scientific contributions were exponentially cumulated to their database. If can analysis important words and related relationships of the papers, a change of research trend in a domain is an interesting topic in text mining. We use a Term Frequency Inverse Document Frequency (TFIDF) to extract meaningful words, the similarity of words measures using WordNet information and a document comparison approach. To measure the similarity from word lists extracted by TFIDF and differences of important word clusters and weights, the approach analyzes the research trend and visualizes the differences of research interest in same research fields. To show usefulness of proposed approach, we illustrate simulations and various results