Suitability of Alternative Approaches to Extracting Wine Sensory Insights from Text Data: A Case Study in Champagne Wines

In wine sensory evaluation, the integration of advanced data analysis techniques with more traditional approaches is essential for addressing challenges and improving practical applications. The current work explores some innovative text mining approaches for obtaining sensory information from various sources. One aspect of interest is creating specialised dictionaries and lexicons through automated processing (e.g., Natural Language Processing/NLP), so that raw text data can be converted into standardised sensory terms understood by specialists. Programmatic processing, in this context, refers to the automated extraction, transformation, consolidation, and analysis of sensory data, significantly reducing human error and allowing for the efficient handling of large data volumes, which is something not possible through manual processing only. The approach is illustrated using diverse data sources, such as structured database-type (for example, from wine sellers) and open-source data from sensory and consumer research publications. The manuscript introduces new ways to draw insights using coupled pipelines for dimension reduction (Multiple Correspondence Analysis/MCA), cluster analysis (Latent Semantic Analysis/LSA), and visual tools (network analysis, chord diagrams, wordvenn diagrams). This manuscript aims to provide practical tools and new insights for sensory specialists looking to enhance their evaluation techniques using text data, showcasing the versatility of advanced analysis techniques in extracting relevant sensory information. These tools use open-source languages such as Python and R and can be further applied for different product spaces.