Digital voice-of-customer processing by topic modelling algorithms: insights to validate empirical results

Purpose: Digital voice-of-customer (digital VoC) analysis is gaining much attention in the field of quality management. Digital VoC can be a great source of knowledge about customer needs, habits and expectations. To this end, the most popular approach is based on the application of text mining algorithms named topic modelling. These algorithms can identify latent topics discussed within digital VoC and categorise each source (e.g. each review) based on its content. This paper aims to propose a structured procedure for validating the results produced by topic modelling algorithms. Design/methodology/approach: The proposed procedure compares, on random samples, the results produced by topic modelling algorithms with those generated by human evaluators. The use of specific metrics allows to make a comparison between the two approaches and to provide a preliminary empirical validation. Findings: The proposed procedure can address users of topic modelling algorithms in validating the obtained results. An application case study related to some car-sharing services supports the description. Originality/value: Despite the vast success of topic modelling-based approaches, metrics and procedures to validate the obtained results are still lacking. This paper provides a first practical and structured validation procedure specifically employed for quality-related applications.