Year: 2020
Type: article
Abstract: Machine learning and artificial intelligence techniques have been increasingly employed in business research to discover or extract new simple features from large and unstructured data. These machine learned features (MLFs) are then used as independent or explanatory variables in the main econometric models for empirical research. Despite this growing trend, there has been little research regarding the impact of using MLFs on statistical inference for empirical research. In this paper, we undertake feature identification and parameter estimation issues related to the use of topics/features extracted by Latent Dirichlet Allocation, a popular machine learning technique for text mining. We propose a novel method to extract features that result in the minimum-variance estimation of the regression model parameters. This enables a better use of unstructured text data for econometric modeling in empirical research. The effectiveness of the proposed method is validated with an experimental evaluation study on real-world text data.
Authors: Xiaoping Liu, Xiao‐Bai Li
Institutions: Northeastern University, University of Massachusetts Lowell
Language: English
Subfield: General Social Sciences
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3119339759