Natural language processing analysis of online reviews for small business: extracting insight from small corpora

Benjamin J. McCloskey (), Phillip M. LaCasse () and Bruce A. Cox ()
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Benjamin J. McCloskey: Air Force Institute of Technology
Phillip M. LaCasse: Air Force Institute of Technology
Bruce A. Cox: Air Force Institute of Technology

Annals of Operations Research, 2024, vol. 341, issue 1, No 12, 295-312

Abstract: Abstract Receiving and acting on customer input is essential to sustaining and growing any service organization, particularly a small family business whose livelihood depends on strong relationships with its customers. The competitive advantage offered by advanced analytical approaches for supporting decisions is not trivial, and enterprises across virtually all domains of society are investing heavily in this emerging discipline. Natural Language Processing (NLP) is a subset of computer science that employs computational approaches to analyze human language; it is effective at extracting insight from text data but frequently requires large corpora to train its models, in the scale of thousands or millions of documents. This restricts its accessibility to those large enterprises with the capability to capture, store, manage, and analyze such corpora. This research explores a pilot study that applies NLP approaches, specifically topic modeling and large language models (LLM), to assist a small, family-owned business in assessing its strengths and weaknesses based on customer reviews. The relevant corpora of online Facebook, Google Reviews, TripAdvisor, and Yelp reviews is far smaller than ideal, numbering only in the hundreds. Results demonstrate that coherent and actionable insights from big-data approaches are obtainable and that small organizations are not automatically excluded from the benefits of these advanced analytical approaches, with complementary employment of both topic modeling and LLM presenting the greatest potential for similarly-positioned organizations to exploit.

Keywords: Natural language processing; Topic modeling; Large language models; ChatGPT; Online reviews (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-023-05816-2

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