Chen, Ziyang2021-05-042021-05-042021https://hdl.handle.net/10182/13762Meat consumption has caused several problems in terms of overusing freshwater, underground water contamination, land degradation, and animal welfare. To mitigate these problems, replacing animal meat products with alternatives such as plant-, insect-, algae-, yeast-fermented-based protein, and cultured meat is an available strategy. To enhance the commercial success of alternative protein products, understanding the sensory profiles and acceptability from consumers is necessary. In traditional sensory tests, conducting descriptive sensory evaluation is expensive and time-consuming. To overcome these drawbacks, text mining and natural language processing are introduced as a novel approach to obtain sensory attributes and rapidly develop a descriptive lexicon. In this study, the application of text mining and natural language processing in alternative protein profiles was explored by analysing alternative proteins’ attributes and descriptive words from n=20 academic papers (that described the recent information of alternative proteins). From 2018 to 2021, plant- and insect-based proteins are the centres of alternative proteins research. Insect-based protein was less popular than plant-based proteins because of food neophobia and psychological barrier. Adults were more likely to accept insect-based protein products. The emotional profile analysis showed that there was no significant association between emotions and protein categories in this study. Our research showed that applying text mining and natural language processing can benefit the descriptive sensory evaluation, which means that it can rapidly obtain and analyse an large amount of data rapidly, thus overcoming traditional lexicon development techniques.enalternative proteinsnatural language processingtext miningdescriptive textsensory attributesinsect proteinsplant proteinsDissertationANZSRC::090802 Food EngineeringANZSRC::080107 Natural Language ProcessingANZSRC::090801 Food Chemistry and Molecular Gastronomy (excl. Wine)Q112954990