Emerging industry classification based on BERT model

Abstract

Accurate industry classification is central to economic analysis and policy making. Current classification systems, while foundational, exhibit limitations in the face of the exponential growth of big data. These limitations include subjectivity, leading to inconsistencies and misclassifications. To overcome these shortcomings, this paper focuses on utilizing the BERT model for classifying emerging industries through the identification of salient attributes within business descriptions. The proposed method identifies clusters of firms within distinct industries, thereby transcending the restrictions inherent in existing classification systems. The model exhibits an impressive degree of precision in categorizing business descriptions, achieving accuracy rates spanning from 84.11% to 99.66% across all 16 industry classifications. This research enriches the field of industry classification literature through a practical examination of the efficacy of machine learning techniques. Our experiments achieved strong performance, highlighting the effectiveness of the BERT model in accurately classifying and identifying emerging industries, providing valuable insights for industry analysts and policymakers.