TY - JOUR
T1 - Utilization of text mining as a big data analysis tool for food science and nutrition
AU - Tao, Dandan
AU - Yang, Pengkun
AU - Feng, Hao
N1 - Funding Information:
This study was partially supported by a USDA Specialty Crop Block Grant award through Illinois Department of Agriculture (698 IDOA SC-19-06) and the Illinois Agricultural Experiment Station.
Publisher Copyright:
© 2020 Institute of Food Technologists®
PY - 2020/3
Y1 - 2020/3
N2 - Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.
AB - Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.
KW - big data
KW - information technology
KW - semantic web
KW - text mining
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U2 - 10.1111/1541-4337.12540
DO - 10.1111/1541-4337.12540
M3 - Review article
C2 - 33325182
AN - SCOPUS:85079729733
VL - 19
SP - 875
EP - 894
JO - Comprehensive Reviews in Food Science and Food Safety
JF - Comprehensive Reviews in Food Science and Food Safety
SN - 1541-4337
IS - 2
ER -