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

Increased meat consumption has been associated with the overuse of fresh water, underground water contamination, land degradation, and negative animal welfare. To mitigate these problems, replacing animal meat products with alternatives such as plant-, insect-, algae-, or yeast-fermented-based prote...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ziyang Chen, Cristhiam Gurdian, Chetan Sharma, Witoon Prinyawiwatkul, Damir D. Torrico
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	alternative proteins text mining natural language processing sentiment analysis Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ca5c4deb125b4ead9cc7b8a5ef5861d7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ca5c4deb125b4ead9cc7b8a5ef5861d7
record_format	dspace
spelling	oai:doaj.org-article:ca5c4deb125b4ead9cc7b8a5ef5861d72021-11-25T17:32:18ZExploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins10.3390/foods101125372304-8158https://doaj.org/article/ca5c4deb125b4ead9cc7b8a5ef5861d72021-10-01T00:00:00Zhttps://www.mdpi.com/2304-8158/10/11/2537https://doaj.org/toc/2304-8158Increased meat consumption has been associated with the overuse of fresh water, underground water contamination, land degradation, and negative animal welfare. To mitigate these problems, replacing animal meat products with alternatives such as plant-, insect-, algae-, or yeast-fermented-based proteins, and/or cultured meat, is a viable strategy. Nowadays, there is a vast amount of information regarding consumers’ perceptions of alternative proteins in scientific outlets. Sorting and arranging this information can be time-consuming. To overcome this drawback, text mining and Natural Language Processing (NLP) are introduced as novel approaches to obtain sensory data and rapidly identify current consumer trends. In this study, the application of text mining and NLP in gathering information about alternative proteins was explored by analyzing key descriptive words and sentiments from <i>n</i> = 20 academic papers. From 2018 to 2021, insect- and plant-based proteins were the centers of alternative proteins research as these were the most popular topics in current studies. Pea has become the most common source for plant-based protein applications, while spirulina is the most popular algae-based protein. The emotional profile analysis showed that there was no significant association between emotions and protein categories. Our work showed that applying text mining and NLP could be useful to identify research trends in recent sensory studies. This technique can rapidly obtain and analyze a large amount of data, thus overcoming the time-consuming drawback of traditional sensory techniques.Ziyang ChenCristhiam GurdianChetan SharmaWitoon PrinyawiwatkulDamir D. TorricoMDPI AGarticlealternative proteinstext miningnatural language processingsentiment analysisChemical technologyTP1-1185ENFoods, Vol 10, Iss 2537, p 2537 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	alternative proteins text mining natural language processing sentiment analysis Chemical technology TP1-1185
spellingShingle	alternative proteins text mining natural language processing sentiment analysis Chemical technology TP1-1185 Ziyang Chen Cristhiam Gurdian Chetan Sharma Witoon Prinyawiwatkul Damir D. Torrico Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
description	Increased meat consumption has been associated with the overuse of fresh water, underground water contamination, land degradation, and negative animal welfare. To mitigate these problems, replacing animal meat products with alternatives such as plant-, insect-, algae-, or yeast-fermented-based proteins, and/or cultured meat, is a viable strategy. Nowadays, there is a vast amount of information regarding consumers’ perceptions of alternative proteins in scientific outlets. Sorting and arranging this information can be time-consuming. To overcome this drawback, text mining and Natural Language Processing (NLP) are introduced as novel approaches to obtain sensory data and rapidly identify current consumer trends. In this study, the application of text mining and NLP in gathering information about alternative proteins was explored by analyzing key descriptive words and sentiments from <i>n</i> = 20 academic papers. From 2018 to 2021, insect- and plant-based proteins were the centers of alternative proteins research as these were the most popular topics in current studies. Pea has become the most common source for plant-based protein applications, while spirulina is the most popular algae-based protein. The emotional profile analysis showed that there was no significant association between emotions and protein categories. Our work showed that applying text mining and NLP could be useful to identify research trends in recent sensory studies. This technique can rapidly obtain and analyze a large amount of data, thus overcoming the time-consuming drawback of traditional sensory techniques.
format	article
author	Ziyang Chen Cristhiam Gurdian Chetan Sharma Witoon Prinyawiwatkul Damir D. Torrico
author_facet	Ziyang Chen Cristhiam Gurdian Chetan Sharma Witoon Prinyawiwatkul Damir D. Torrico
author_sort	Ziyang Chen
title	Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
title_short	Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
title_full	Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
title_fullStr	Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
title_full_unstemmed	Exploring Text Mining for Recent Consumer and Sensory Studies about Alternative Proteins
title_sort	exploring text mining for recent consumer and sensory studies about alternative proteins
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ca5c4deb125b4ead9cc7b8a5ef5861d7
work_keys_str_mv	AT ziyangchen exploringtextminingforrecentconsumerandsensorystudiesaboutalternativeproteins AT cristhiamgurdian exploringtextminingforrecentconsumerandsensorystudiesaboutalternativeproteins AT chetansharma exploringtextminingforrecentconsumerandsensorystudiesaboutalternativeproteins AT witoonprinyawiwatkul exploringtextminingforrecentconsumerandsensorystudiesaboutalternativeproteins AT damirdtorrico exploringtextminingforrecentconsumerandsensorystudiesaboutalternativeproteins
_version_	1718412254467063808

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

Ejemplares similares