Description
Understanding how consumers truly perceive products is at the heart of sensory and consumer science. While traditional surveys rely on limited, predefined response options, open-ended questions invite participants to describe their sensory experiences in their own words, offering richer and more nuanced insights into individual perceptions and preferences. Building on this shift toward more expressive forms of data researchers now have access to vast, unstructured text data from online reviews to social media posts offering unprecedented opportunities to explore consumer opinions and sentiments. Thanks to advancements in natural language processing, the use of text data is also rapidly gaining traction. Collection and Analysis of Text Data in Sensory and Consumer Science provides a comprehensive overview of modern text-based research methods. From experimental approaches to data collection, to the reuse of large-scale online corpora, and their analysis through methods such as multiple-response correspondence analysis, sentiment analysis, co-occurrence networks, and netnography, this volume explores a broad array of tools and methodologies. It also adopts the multidisciplinary perspective the subject requires, drawing from neuroscience, psychology, and knowledge engineering to deepen our understanding of language, perception, and text data structuring. For researchers aiming to enrich their understanding of consumer perception, Collection and Analysis of Text Data in Sensory and Consumer Science will prove a novel and invaluable guide.
Introduction.- Fundamentals On Natural Language And Food Perception.- Handling Text Data.- Food Related Text Data Collection Experimental Aspects, Analysis And Use Cases.- Reuse Of Large Corpus Of Food Related Text Data Use Cases.
Michel Visalli is a research engineer at the Centre for Taste and Feeding Behavior (National Research Institute for Agriculture, Food and Environment). He holds a Master s degree in Computer Science and Statistics, and a PhD in Sensory and Consumer Science. He develops innovative research methods and software grounded in real-life contexts, combining experimental, behavioral, and data-driven approaches. His work incorporates FAIR-by-design principles and spans temporal sensory perception and affective measurements, natural language processing, indirect methods for assessing preferences, and the analysis of food consumption practices. He has published extensively in leading journals and serves on the editorial boards of several journals.
Mara Galmarini is a Food Technologist (UCA) with a PhD in Bromatology from the University of Buenos Aires. She is an Independent Researcher at the National Scientific and Technical Research Council (CONICET) in Argentina and a full-time professor at Universidad Torcuato Di Tella in sensory science and consumer behavior. A former Marie Curie Fellow, she has authored numerous scientific publications in peer-reviewed journals and participated in international research collaborations with focus on sensory methods and cross-cultural studies. She teaches undergraduate and graduate courses in behavioral science, and actively promotes science communication through social media, tv shows, public talks, and writing in national newspapers
