Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web Science) database uses a novel method topic classification to explore vocabulary characteristics classified articles. The corpora are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed automatically classify articles; co-word techniques used show feature words for distinct clusters, titles, consistency Based on results 5783 abstracts, classifies number clusters into six categories: travel, culture, sustainability, model, behavior, hotel. cross-check applied assess classifications, list titles keywords documents with three smallest distances in each category apply strategic diagram present features categories.