Using “Text Mining” Analysis for the Assessment of the Health Quality of Dietary Supplements

Techniques of text data analysis have been known for many years and commonly used in many areas of life. Text mining enables, among others, the acquisition of information from the text, its filtering, and studying of similarities and relationships. The aim of this paper is to design a method that would make it possible to assess the health quality of dietary supplements, on the basis of text mining techniques. A fictional plant-based product was used in the study, which was compared with other products containing at least one of the tested ingredients registered in the years 2007–2019 in the register of dietary supplements kept by the Chief Sanitary Inspectorate (GIS), which were given either the “consistent” or “to be clarified” status. The obtained results concern the frequency of occurrence of the individual ingredients (St John’s wort/Hypericum, melissa, rose root/Rhodiola) in other products, considering their status in the register. The data thus obtained was subjected to classical statistical analysis in order to find correlations between the presence of a given ingredient and the product status. In view of the obtained results, the text mining analysis may be considered as a helpful tool in the process of internal risk assessment performed by manufacturers of dietary supplements.