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.