Abstract

Objectives: Topic modeling is a machine learning method that has been used in disciplines like social sciences or the industrial production sector. With topic modeling, a scientist can reduce many articles to a few topics to get an overview of a specific field (e.g., for a scoping review). The objectives of this paper were (1) to demonstrate the applicability of topic modeling to the field of anthropology by a new framework and (2) to present a new method for determining the optimal number of topics used.

Subjects and methods: The documents used in this paper were collected from the database IEEE, using the search term “anthropology” to obtain a broad range of topics. Topic modeling was performed by Latent Dirichlet Allocation (LDA) method, using R. To determine the optimal candidate of topics (k), a mathematical formula based on the slope of the perplexity curve was established.

Results: The application of the framework to the corpus of 518 documents was able to sort all documents into 15 research areas with little time investment by the researcher while using a standard laptop computer. The process of semantic validation was successfully done for all 15 topics.

Conclusion: The presented framework with the optimal number of topics k enables scientists in the field of anthropology to perform a scoping review and thus spend less time to manually categorize documents. Topic modeling can be used by researchers in multidisciplinary projects to improve understanding content in a faster way.