Mapping and predicting research trends in international journal publications using graph and topic modeling

Abstract

Researchers and journal managers need summary information, such as research maps and trends. Topic and words-based document content analysis alternative to science mapping and trend prediction based on bibliographic analysis. The data are a collection of journal articles/proceeding documents and metadata for 2011-2020 published by the International Journal of Electrical and Computer Engineering (IJECE). A combination of several techniques and methods is used, such as text mining, topic modeling, cosine similarity, network analysis, graph theory, and seasonal autoregressive integrated moving average (SARIMA). This research has produced research topics, mapping, trend prediction, and visualization according to its objectives. The results of the topic coherence test obtained the optimal number of topics, as many as 6. The results of the topics were evaluated by experts to be given labels and areas of focus. On the research map for each topic, information is found on trending research, the most popular research, research that is central to the research group, and critical research on the development of the group path. It also identifies the type of breakthrough, incremental, and research gap. Predictions of research trends obtained are based on topics and words that describe the development of research. Visualization is descriptive and predictive.