Abstract: The specialty coffee shop market in Toronto has become increasingly competitive, making it essential for business owners to understand the factors that drive customer satisfaction and differentiation. This study aims to identify the main themes expressed in Google Maps reviews of Toronto’s specialty coffee shops over the past year, providing actionable insights for entrepreneurs and industry stakeholders. Over 5000 customer reviews were analyzed using BERTopic (Bidirectional Encoder Representations from Transformers Topic), a state-of-the-art topic modeling approach that leverages contextual language understanding to extract clear and meaningful topics from large volumes of text. The analysis revealed distinct positive themes, such as cozy atmospheres and high-quality coffee, as well as negative aspects like unfriendly service and poor value for money. By correlating these topics with review ratings, the study highlights specific opportunities for improvement and differentiation in the market. These findings offer practical value for business planning, enabling coffee shop owners to make data-driven decisions and enhance customer experiences in a crowded urban landscape. Beyond its local insights, this research introduces a scalable analytical framework that can be applied to market research, business planning, and feasibility studies in diverse sectors, empowering others to extract actionable intelligence from large volumes of unstructured textual data.
Keywords: Topic modeling, BERTopic, Google reviews, Specialty Coffee
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DOI:
10.17148/IJARCCE.2025.14709
