Investigating the Use of Topic Modeling for Social Media Market Research: A South African Case Study

Businesses are increasingly investigating the use of data science and machine learning techniques for market research. This paper investigates the use of topic modeling as a tool for social media market research, specifically the influence and impact of this technology within market research practice. As an example of the use of topic modeling, three different topic modeling algorithms are applied to a single dataset extracted from Reddit, and their performance compared. The latent Dirichlet allocation (LDA) algorithm was trained as a baseline and compared to the Correlated topic model (CTM) and the Gibbs sampling for Dirichlet multinomial mixtures (GSDMM) model. The CTM outperformed the LDA model, while the GSDMM was unable to improve on the baseline. The 25 topics produced by the final CTM were investigated in greater detail and interpreted within the context of market research. Although five of these topics did not prove useful, the remaining topics were easily interpreted and divided into six categories related to (1) features, (2) software, (3) acquisition, (4) workouts, (5) physical design, and (6) physiological monitoring. Each category’s topics were able to provide valuable insight regarding consumers’ opinions about and experiences of the related product.