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What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Author

Listed:
  • Youzhu Li

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Xianghui Gao

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Mingying Du

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
    College of Accounting, Wuchang Institute of Technology, Wuhan 430065, China)

  • Rui He

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Shanshan Yang

    (College of Media and Art Design, Wuhan Donghu University, Wuhan 430212, China)

  • Jason Xiong

    (Walker College of Business, Appalachian State University, Boone, NC 28608, USA)

Abstract

(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

Suggested Citation

  • Youzhu Li & Xianghui Gao & Mingying Du & Rui He & Shanshan Yang & Jason Xiong, 2020. "What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China," Sustainability, MDPI, vol. 12(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1345-:d:319704
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    References listed on IDEAS

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    1. Xiaoqin Zhu & Xiaofei Xie, 2015. "Effects of Knowledge on Attitude Formation and Change Toward Genetically Modified Foods," Risk Analysis, John Wiley & Sons, vol. 35(5), pages 790-810, May.
    2. Jikun Huang & Bowen Peng & Xiaobing Wang, 2017. "Scientists’ attitudes toward agricultural GM technology development and GM food in China," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 9(3), pages 369-384, September.
    3. James O. Bukenya & Natasha R. Wright, 2007. "Determinants of consumer attitudes and purchase intentions with regard to genetically modified tomatoes," Agribusiness, John Wiley & Sons, Ltd., vol. 23(1), pages 117-130.
    4. Xi Lu & Xiaofei Xie & Ji Xiong, 2015. "Social trust and risk perception of genetically modified food in urban areas of China: the role of salient value similarity," Journal of Risk Research, Taylor & Francis Journals, vol. 18(2), pages 199-214, February.
    5. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    6. Ling Wang & Gongliang Hu & Tiehua Zhou, 2018. "Semantic Analysis of Learners’ Emotional Tendencies on Online MOOC Education," Sustainability, MDPI, vol. 10(6), pages 1-19, June.
    7. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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    Cited by:

    1. Chun-Chieh Ma & Han-Shen Chen & Hsiao-Ping Chang, 2020. "Crisis Response and Supervision System for Food Security: A Comparative Analysis between Mainland China and Taiwan," Sustainability, MDPI, vol. 12(7), pages 1-13, April.
    2. Taesoo Cho & Taeyoung Cho & Guosong Zhao & Hao Zhang, 2020. "The Impact of South Korea Golf Resort Social Network Services Advertising and Online Word of Mouth on Consumer Brand Value," Sustainability, MDPI, vol. 12(11), pages 1-13, May.
    3. Marcela Korenkova & Milan Maros & Michal Levicky & Milan Fila, 2020. "Consumer Perception of Modern and Traditional Forms of Advertising," Sustainability, MDPI, vol. 12(23), pages 1-25, November.

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