Topic Space Trajectories: A case study on machine learning literature
This provides an interpretable tool for researchers to analyze publication trends, though it is incremental as it builds on existing methods like non-negative matrix factorization.
The authors tackled the challenge of tracking research topics in the rapidly growing scientific literature by introducing topic space trajectories, a structure for comprehensible tracking, and demonstrated its applicability on a 50-year corpus of machine learning publications from 32 venues.
The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories, a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.