Confirmatory aspect-level opinion mining processes for tourism and hospitality research: a proposal of DiSSBUS

Jongho Im, Taikgun Song, Youngsu Lee, Jewoo Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

We proposed a new rule-based text analysis method to effectively summarize and transform unstructured user-generated content (online customer reviews) into an analysable form for tourism and hospitality research. To differentiate this method, we developed the Disintegrating, Summarizing, Straining, Bagging, Upcycling, and Scoring–DiSSBUS–algorithm which can address the following problems in previous approaches: (1) false identification of irrelevant aspect terms, (2) improper handling of multiple aspects and sentiments within a text unit, and (3) data sparsity. The algorithm’s distinctive advantage is to decompose a single review into a set of bi-terms related to the aspects that are pre-specified based on domain knowledge. Therefore, this algorithm can identify customer opinions on specific aspects, which allows to extract variables of interest from online reviews. To evaluate the performance of our confirmatory aspect-level opinion-mining algorithm, we applied it to customer reviews on restaurants in Hawaii. The findings from the empirical test validated its effectiveness.

Original languageEnglish
Pages (from-to)1876-1894
Number of pages19
JournalCurrent Issues in Tourism
Volume25
Issue number12
DOIs
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Tourism, Leisure and Hospitality Management

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