Identifying Worldwide Interests in Organic Foods by Google Search Engine Data

IEEE Access

Bin Li¹  Jinglu Tan²  Ye Su³  Meiying Zhong⁴  Ya Guo⁴  Xu Liu⁴  Juan Liu⁵  Seyed Mohammad Taghi Gharibzahedi⁶ 

[1] Beijing Research Center for Information Technology in Agriculture, Beijing, China;Department of Bioengineering, University of Missouri, Columbia, MO, USA;Department of Economics, University of Nebraska Kearney, Kearney, NE, USA;Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, China;School of Foreign Languages and Tourism, Wuxi Institute of Technology, Wuxi, China;Young Researchers and Elites Club, Lahijan Branch, Islamic Azad University, Lahijan, Iran;

关键词: Organic food;    search engine;    search interest;    neural network;    data modeling;    deep learning;   

DOI  :  10.1109/ACCESS.2019.2945105

来源: DOAJ

【 摘 要 】

Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers' interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people's interests in major organic foods over time.

【 授权许可】

Unknown