Izvorni znanstveni članak
https://doi.org/10.37741/t.72.1.1
Analyzing Tourism Online Reviews: An Extended Approach to Hierarchical Topic Detection Using Keyword Clustering
Wolfram Höpken
orcid.org/0000-0002-4175-1295
; Institute for Digital Transformation, Ravensburg-Weingarten University of Applied Sciences, Weingarten, Germany
Matthias Fuchs
; Department of Business Economics, Law, Geography and Tourism/ETOUR, Mid-Sweden University, Östersund, Sweden
Maria Lexhagen
orcid.org/0000-0002-6610-9303
; Department of Business Economics, Law, Geography and Tourism/ETOUR, Mid-Sweden University, Östersund, Sweden
Sažetak
Tourism managers are increasingly turning to the online sphere to gain relevant customer insights. However, current approaches to analyzing vast and rapidly changing user-generated content (UGC) face several limitations. Supervised approaches require significant effort to provide pre-tagged training data and cannot dynamically identify topics mentioned in UGC. On the other hand, unsupervised approaches typically do not support different abstraction levels or enable a successive refinement of analysis in a drill-down manner, which is often expected as a practical requirement of tourism and destination management. Our research objective is, therefore, to extend current supervised approaches for identifying predefined topics by adopting unsupervised approaches using cluster analysis. The results emphasize that unsupervised approaches can (1) detect non-predefined topics dynamically with an accuracy similar to supervised approaches, thus demonstrating the potential to replace them and avoid the necessity of providing pre-tagged training data. (2) To build a topic hierarchy, unsupervised approaches sense more fine-grained topics as an enhancement of predefined topics on a lower level of abstraction, enabling more powerful drill-down-like analyses. Overall, the proposed extended approach to topic detection promises to support tourism management by meaningfully analyzing the increasing mass of visitors’ online feedback.
Ključne riječi
topic detection; topic hierarchy; keyword clustering; user-generated content; tourism online reviews
Hrčak ID:
313836
URI
Datum izdavanja:
30.1.2024.
Posjeta: 311 *