Information-Seeking Argument Mining: A Step Towards Identifying Reasons in Textual Analysis to Improve Services

Abstract

Service providers increasingly use textual analysis such as sentiment mining or topic models on unstructured data. Still, those techniques fall short when providing linguistic relations such as reasons behind changes in sentiment or topics. Information-seeking argument mining (IS AM) is a text mining technique that automatically extracts and identifies the argumentative structures (e.g., reasoning) from natural language text. So far, however, service researchers and managers hardly use IS AM. This article outlines how to use IS AM to improve services. The empirical study applies IS AM to news articles about scooter-sharing systems, i.e., a service enabling the short-term rentals of electric motorized scooters. The results outline that (i) arguments differ strongly across time, providers of scooter-sharing systems, and media, (ii) knowledge of arguments enable to improve services and communications with customers, and (iii) results from sentiment analysis support the validity of IS AM. The article closes with an outlook for further research.