中文    English

Journal of Library and Information Science in Agriculture ›› 2023, Vol. 35 ›› Issue (7): 94-104.doi: 10.13998/j.cnki.issn1002-1248.23-0312

Previous Articles     Next Articles

Construction and Application of the Attention Analysis Model of Brand Management Policies of Agricultural Products with Geographical Indications

HUO Mengjia1, LIU Juan1,2,*, Huang Jie1   

  1. 1. Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100080;
    2. Key Laboratory of Agricultural Big Data, Ministry of Agriculture, Beijing 100080
  • Received:2023-05-18 Online:2023-07-05 Published:2023-09-20

PDF (PC)

141

Abstract: [Purpose/Significance] Geographical indications (GIs) are an important tool for local governments in China to carry out brand building of agricultural products. Brand management is a continuous systematic project involving multiple subjects. Among them, the problem of government policy attention in the field of brand management of agricultural products with GIs deserves in-depth study. This paper aims to construct a policy attention analysis model in the field of brand management of GI agricultural products based on natural language processing technology. This model provides technical support for local governments to explore the status quo of local GI agricultural products' brand management, analyze the distribution of policy attention, and assist in optimizing strategies to promote their products' brand development. [Method/Process] The study is focused on the distribution of attention paths of GI agricultural products' brand management policies from the perspective of the local government: an analysis model of brand management policies of GI agricultural products was constructed in order to support the local government to carry out the analysis of the status quo of local agricultural products' brand management and policy optimization, and provide decision-making support for the optimization of brand management measures of the local agricultural products. First, this paper built a basic corpus based on the Python crawler technology, collected authoritative public information on the Internet, utilized the domain dictionary and UIE general information extraction framework to extract the text of management measures published in local government policies, and built a database of brand management measures of GI agricultural product. Second, a classification model of brand management measures of GI agricultural products based on the Transformer model was constructed. Third, this paper built a classification model based on the Transformer model, which can classify the extracted brand management measures of agricultural products and construct the policy attention distribution map. Finally, based on the policy attention distri-bution given by the model, we can find the brand management bottlenecks and recommend countermeasures to solve the bottlenecks. [Results/Conclusions] This paper takes Yantai apples as an example for model validation. After extracting and categorizing Yantai apples' brand management data, it is found that the policy attention of Yantai apples is more con-centrated, and the measures are highly similar, with 41.1% of the text of the measures focused on the part of brand positioning and planning, 31.7% on the part of brand competitiveness enhancement, and less than 10% on the part of brand marketing and protection. It can be seen that the brand effect of Yantai apples with GIs has not been well utilized.

Key words: geographical indications of agricultural products, natural language processing, policy attention, information extraction, text classification

CLC Number: 

  • F303.3
[1] 农业农村部. 关于开展2022年农业品牌精品培育工作的通知: 农办市[2022]10号.[2022-06-30]. . 关于开展2022年农业品牌精品培育工作的通知: 农办市[2022]10号.[2022-06-30]. http://www.moa.gov.cn/govpublic/SCYJJXXS/202206/t20220630_6403818.htm.
[2] 詹君恒. 原产地效应及其作用机制: 一个文献综述[J]. 中国市场, 2019(16): 19-22.
ZHAN J H.Origin effect and its mechanism: A literature review[J]. China market, 2019(16): 19-22.
[3] 侯雅洁. 农业品牌建设将走进标准引领新时代[J]. 农业知识, 2022(10): 40, 42.
HOU Y J. Agricultural brand construction will enter the standard and lead the new era[J]. Agriculture knowlege, 2022(10): 40, 42.
[4] 房玲玲, 杨颖秀. 师范生公费教育政策文本分析及政策建议[J]. 延边大学学报(社会科学版), 2020, 53(1): 124-132, 144.
FANG L L, YANG Y X.Text analysis of the free education policy for normal students and policy suggestion[J]. Journal of Yanbian university (social sciences), 2020, 53(1): 124-132, 144.
[5] 宋娇娇, 徐芳, 孟溦. 中国科技评价政策的变迁与演化:特征、主题与合作网络[J]. 科研管理, 2021, 42(10): 11-19.
SONG J J, XU F, MENG W.Changes and evolutions of science and technology evaluation policies in China: Characteristics, subjects and collaborative networks[J]. Science research management, 2021, 42(10): 11-19.
[6] 王长征, 彭小兵, 彭洋. 地方政府大数据治理政策的注意力变迁——基于政策文本的扎根理论与社会网络分析[J]. 情报杂志, 2020, 39(12): 111-118.
WANG C Z, PENG X B, PENG Y.Attention evolution of big data governance policies of local government - Based on grounded theory and social network[J]. Journal of intelligence, 2020, 39(12): 111-118.
[7] 俞兆达. 新时代中国高等教育质量政策焦点的质化考察[J]. 当代教育论坛, 2022(2): 1-11.
YU Z D.A qualitative study on the focus of China's higher education quality policy in the new era[J]. Forum on contemporary education, 2022(2): 1-11.
[8] 徐宛笑, 李文欣, 黄栋. 中国—东盟文化产业合作中地方政府注意力分配研究——基于地方政府政策文本分析[J]. 东南亚纵横, 2022(5): 93-104.
XU W X, LI W X, HUANG D.A study of the attention allocation of local governments in China-ASEAN cultural industry cooperation: In the case of local government policy texts[J]. Crossroads: Southeast Asian studies, 2022(5): 93-104.
[9] 朱斌, 宁伟, 张静雅, 等. 我国基本医保门诊慢特病政策分析——基于内容分析法[J]. 中国卫生政策研究, 2022, 15(1): 43-49.
ZHU B, NING W, ZHANG J Y, et al.Policy analysis of basic health insurance chronic disease and special critical diseases security in China: Based on content analysis[J]. Chinese journal of health policy, 2022, 15(1): 43-49.
[10] SUN Y, WANG S H, FENG S K, et al.ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation[J]. arXiv preprint arXiv:2107.02137, 2021.
[11] DAI Z H, YANG Z L, YANG Y M, et al.Transformer-XL: Attentive language models beyond a fixed-length context[J]. arXiv preprint arXiv:1901.02860, 2019.
[12] CHEN X R, YUILLE A L. Detecting and reading text in natural scenes[C]// Proceedings of the2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR. Piscataway, New Jersey: IEEE, 2004: II.
[13] MILWARD D, THOMAS J.From information retrieval to information extraction[C]// Proceedings of the ACL-2000 workshop on Recent advances in natural language processing and information retrieval held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Morristown, NJ, USA: Association for Computational Linguistics, 2000: 85-97.
[14] VASWANI A, SHAZEER N, PARMAR N, et al.Attention is all You need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. New York: ACM, 2017: 6000-6010.
[15] 孙楚权. 基于Transformer的文本分类算法及应用[D]. 烟台: 山东工商学院, 2021.
SUN C Q.Algorithm and application of text classification based on transformer[D]. Yantai: Shandong Technology and Business University, 2021.
[1] AN Bo. Literature Classification Methods based on Structural Information Enhancement [J]. Journal of Library and Information Science in Agriculture, 2023, 35(3): 15-24.
[2] SHI Yunlai, CUI Yunpeng, DU Zhigang. A Classification Method of Agricultural News Text Based on BERT and Deep Active Learning [J]. Journal of Library and Information Science in Agriculture, 2022, 34(8): 19-29.
[3] MAO Jin, CHEN Ziyang. A Deep Learning Based Approach to Structural Function Recognition of Scientific Literature Abstracts [J]. Journal of Library and Information Science in Agriculture, 2022, 34(3): 15-27.
[4] WANG Weiwei, HUA Bolin. Extraction and Mining of Intelligent Description Information of Public Culture [J]. Journal of Library and Information Science in Agriculture, 2021, 33(8): 13-23.
[5] SUN Tan, DING Pei, HUANG Yongwen, XIAN Guojian. Review on the Application and Development Strategies of Text Mining in Agriculture Knowledge Services [J]. Journal of Library and Information Science in Agriculture, 2021, 33(1): 4-16.
[6] LUO Xin. Comparative Study of Chinese Text Classification Model based on Particle Swarm Intelligence [J]. , 2018, 30(4): 18-22.
[7] XIE Na. The Depth Information Identification System of Library Service Robots’ Voice Commands [J]. , 2016, 28(2): 48-50.
[8] LUO Xin. Research on text Classification Model Based on Random Forests [J]. , 2016, 28(11): 50-53.
[9] CHEN Shu-ping. Automatic Extraction of Metadata Information for Dissertation based on Feature and Rule Pattern [J]. , 2015, 27(2): 57-59.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of Library and Information Science in Agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议