Obtaining Insights from Museum Review
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Abstract
The study aimed to obtain insights from museum reviews regarding topics that were discussed among over 300,000 reviewers between 2007 and 2020. Additionally, given the review rating, the study aimed to investigate reasons for those who gave low review ratings. We used secondary data of museum reviews scraping from TripAdvisor. The topic modeling technique of Latent Dirichlet Allocation (LDA) was used to systematically extract topics from text. The computer algorithm assigned each topic a probability distribution over vocabularies and clustered each topic in an unsupervised mechanism based on saliency and relevance. We then visualized our result in an Inter-topic Distance Map. According to our findings, there were seven broad topics that reviewers talked about including “Art”, “Service”, “Dining”, “History”, “Visitor”, “Animal” and “Vehicle”. To be more specific, around 40% of topic contribution involved topics of “service”, “visitor” and “dinning”. These topics' expectations could be managed and improved by sets of marketing strategy in terms of the marketing mix to satisfy customer needs and feedbacks. While the monetary value of museum experiences was a concern for those who gave a low rating. Hence, we recommended the use of a customized pricing strategy which is likely to commensurate with customer expectation as well as increase museum operation efficiency.
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