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Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins.

Ziyang Chen1, Cristhiam Gurdian2, Chetan Sharma1

  • 1Centre of Excellence-Food for Future Consumers, Department of Wine, Food and Molecular Biosciences, Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, New Zealand.

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Summary

Text mining and Natural Language Processing (NLP) efficiently analyze consumer perceptions of alternative proteins. This approach rapidly identifies research trends in plant- and insect-based proteins, overcoming limitations of traditional sensory studies.

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alternative proteinsnatural language processingsentiment analysistext mining

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Area of Science:

  • Food Science and Technology
  • Consumer Behavior
  • Computational Linguistics

Background:

  • Growing meat consumption raises environmental and animal welfare concerns.
  • Alternative proteins (plant-, insect-, algae-, yeast-fermented, cultured meat) offer sustainable solutions.
  • Vast scientific literature exists on consumer perceptions of these alternatives.

Purpose of the Study:

  • To explore the application of text mining and Natural Language Processing (NLP) for analyzing consumer perceptions of alternative proteins.
  • To rapidly gather and synthesize information from academic research on alternative proteins.
  • To identify current research trends and popular alternative protein sources.

Main Methods:

  • Analysis of 20 academic papers published between 2018 and 2021.
  • Application of text mining and NLP techniques to extract key descriptive words and sentiments.
  • Identification of popular protein categories, sources, and associated emotional profiles.

Main Results:

  • Insect- and plant-based proteins were the most researched alternative proteins from 2018-2021.
  • Pea emerged as the most common plant-based protein source; spirulina was the most popular algae-based protein.
  • No significant association was found between specific emotions and alternative protein categories.

Conclusions:

  • Text mining and NLP are effective tools for identifying research trends in sensory studies of alternative proteins.
  • These computational methods accelerate data acquisition and analysis compared to traditional sensory techniques.
  • The study highlights the utility of NLP in navigating the growing body of research on sustainable food alternatives.