A Trend Analysis of Significant Topics Over Time in Machine Learning Research

A vast number of research papers on numerous topics publish every year in different conferences and journals. Thus, it is difficult for new researchers to identify research problems and topics manually, which research community is currently focusing on. Since such research problems and topics help researchers to be updated with new topics in research, it is essential to know trends in research based on topic significance over time. Therefore, in this paper, we propose a method to identify the trends in machine learning research based on significant topics over time automatically. Specifically, we apply a topic coherence model with latent Dirichlet allocation (LDA) to evaluate the optimal number of topics and significant topics for a dataset. The LDA model results in topic proportion over documents where each topic has its probability (i.e., topic weight) related to each document. Subsequently, the topic weights are processed to compute average topic weights per year, trend analysis using rolling mean, topic prevalence per year, and topic proportion per journal title. To evaluate our method, we prepare a new dataset comprising of 21,906 scientific research articles from top six journals in the area of machine learning published from 1988 to 2017. Extensive experimental results on the dataset demonstrate that our technique is efficient, and can help upcoming researchers to explore the research trends and topics in different research areas, say machine learning.