Natural Language Processing Analysis of Online Reviews for Small Business: Extracting Insight from Small Corpora
Document Type
Article
Publication Date
1-27-2024
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.
Source Publication
Annals of Operations Research
Recommended Citation
McCloskey, B. J., LaCasse, P. M., & Cox, B. A. (n.d.). Natural language processing analysis of online reviews for small business: extracting insight from small corpora. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05816-2
Comments
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