Structural review of relics tourism by text mining and machine learning

Abstract

Purpose: The objective of the paper is to find trends of research in relic tourism-related topics. Specifically, this paper uncovers all published studies having latent issues with the keywords "relic tourism" from the Web of Science database. Methods: A total of 109 published articles (2002-2021) were collected related to "relic tourism." Machine learning tools were applied. Network analysis was used to highlight top researchers in this field, their citations, keyword clusters, and collaborative networks. Text analysis and Bidirectional Encoder Representation from Transformer (BERT) of artificial intelligence model were used to predict text or keyword-based topic reference in machine learning. Results: All the papers are published basically on three primary keywords such as "!relics," "culture," and "heritage." Secondary keywords like "protection" and "development" also attract researchers to research this topic. The co-author network is highly significant for diverse authors, and geographically researchers from five countries are collaborating more on this topic. Implications: Academically, future research can be predicated with dense keywords. Journals can bring more special issues related to the topic as relic tourism still has some unexplored areas.