Public Opinion Mining and Analysis of Shine-Muscat Grapes Based on Weibo Big Data

  • FENG JianYing ,
  • MIAO JingBang ,
  • YANG ZiHan ,
  • ZHANG Le ,
  • MU WeiSong
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  • China Agricultural University, Beijing 100083, China

Received date: 2025-04-28

  Revised date: 2025-07-15

  Online published: 2025-12-26

Abstract

In recent years, the Shine-Muscat grape has rapidly expanded in the Chinese market due to its unique taste. However, the expansion of its cultivation scale has led to issues such as quality differentiation and price fluctuations, which have adversely affected the healthy and sustainable development of the industry. This study systematically explores public sentiment dynamics regarding Shine-Muscat grapes based on a large dataset of Weibo posts, using Latent Dirichlet Allocation (LDA) for topic modeling of comment texts and SnowNLP for consumer sentiment analysis. The findings reveal that public attention is primarily focused on price declines, taste variations, and pesticide residue disputes. Topic analysis identifies three core themes: variety characteristics and inter-variety comparisons, sensory quality and safety, and price and consumption experience. Sentiment analysis indicates that negative sentiments slightly outnumber positive ones, with negative emotions primarily stemming from dissatisfaction with taste, declining quality, and safety concerns, while positive evaluations highlight characteristics such as seedlessness and juiciness. By understanding public concerns and sentiment tendencies, this study provides a reference for the rational planning of the development of the Shine-Muscat grape industry, the adjustment of cultivation practices, and the improvement of product characteristics.

Cite this article

FENG JianYing , MIAO JingBang , YANG ZiHan , ZHANG Le , MU WeiSong . Public Opinion Mining and Analysis of Shine-Muscat Grapes Based on Weibo Big Data[J]. Journal of Agricultural Big Data, 2025 , 7(4) : 496 -505 . DOI: 10.19788/j.issn.2096-6369.000113

References

[1] 潘照, 周文化, 肖玥惠子. 基于主成分分析的不同种鲜食葡萄品质评价. 食品与机械, 2018, 34(9):139-146.
  PAN Z, ZHOU W, XIAO Y. Quality evaluation of different fresh grape varieties based on principal component analysis. Food and Machinery, 2018, 34(9):139-146.
[2] 曹琬迪. “阳光玫瑰”葡萄产业发展现状与对策分析. 热带农业科学, 2023, 43(5):126-131.
  CAO W. Analysis on current situation and countermeasures of "Shine-Muscat" grape industry development. Tropical Agricultural Sciences, 2023, 43(5): 126-131.
[3] 李华, 陈晓燕, 王志刚. 阳光玫瑰葡萄早采现象对产业发展的影响及对策分析——基于山东半岛产区实地调研. 中国农村经济, 2022, 8(8):45-58.
  LI H, CHEN X, WANG Z. Impact of early harvesting on "Shine-Muscat" grape industry development in Shandong Peninsula and countermeasures - based on field research in Shandong Peninsula. Chinese Rural Economy, 2022, 8(8):45-58.
[4] 程大伟, 何莎莎, 李正阳, 等. ‘阳光玫瑰’葡萄果实质量分级评价研究. 江西农业学报, 2020, 32(7):30-35.
  CHENG D, HE S, LI Z, et al. Quality grading evaluation of "Shine-Muscat" grape fruit. Jiangxi Agricultural Journal, 2020, 32(7): 30-35.
[5] 尚泓泉, 娄玉穗, 王琰. “阳光玫瑰”葡萄品种特性及花果管理关键技术. 北方园艺, 2023(4):153-157.
  SHANG H, LOU Y, WANG Y. Characteristics of "Shine-Muscat" grape variety and key techniques of flower and fruit management. Northern Horticulture, 2023(4):153-157.
[6] 聂瑞洁. ‘阳光玫瑰’葡萄未来市场形势分析. 北方果树, 2021(6): 53-55.
  NIE R. Analysis of the future market situation of 'Shine-Muscat' grapes. Northern Fruit Trees, 2021(6):53-55.
[7] 清扬. 中国果业的“阳光玫瑰”时代. 中国果业信息, 2024, 41(9):1-17.
  QING Y. The "Shine-Muscat" era of China's fruit industry. China Fruit Industry Information, 2024, 41(9):1-17.
[8] 张敏, 刘涛, 王思琦. 社交媒体意见表达与公众舆情分析——以微博在中国公共话语中的作用为例. 新闻大学, 2023, 2(2):112-128.
  ZHANG M, LIU T, WANG S. Social media opinion expression and public opinion analysis - Taking Weibo's role in Chinese public discourse as an example. Journalism University, 2023, 2(2):112-128.
[9] 钟懿博, 农健, 杜艳华. 基于LDA主题模型的农产品电商评论文本分类分析. 甘肃农业, 2023(12):64-68.
  ZHONG Y, NONG J, DU Y. Analysis of text classification of agricultural products e-commerce comments based on LDA topic model. Gansu Agriculture, 2023(12):64-68.
[10] 熊璐, 高丽芳, 曹兵斌. 基于在线评论的生鲜农产品购买需求主题挖掘——以淘宝芒果产品为例. 农村经济与科技, 2023, 34(9):242-245.
  XIONG L, GAO L, CAO B. Topic mining of fresh agricultural products purchase demand based on online comments - Taking Taobao mango products as an example. Rural Economy and Technology, 2023, 34(9): 242-245.
[11] LIU H, YU Z K, ZHONG X Z, et al. Network public opinion monitoring system for agriculture products based on big data. Scientific Programming, 2021:1-17.
[12] 李颖, 李道亮, 傅泽田. 农产品质量安全网络舆情传播特征与治理策略研究. 中国农业大学学报(社会科学版), 2017, 34(3):113-122.
  LI Y, LI D, FU Z. Research on network public opinion propagation characteristics and governance strategies of agricultural products quality safety. Journal of China Agricultural University (Social Science Edition), 2017, 34(3):113-122.
[13] BLEI D M, NG A Y, JORDAN M I, et al. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3:993-1022.
[14] ZHANG C, WANG H, CAO L, et al. A hybrid term-term relations analysis approach for topic detection. Knowledge-Based Systems, 2016, 93:109-120.
[15] 蔡增玉, 韩洋, 张建伟, 等. 基于SnowNLP的微博网络舆情分析系统. 科学技术与工程, 2024, 24(13):5457-5464.
  CAI Z, HAN Y, ZHANG J, et al. Weibo network public opinion analysis system based on snowNLP. Science Technology and Engineering, 2024, 24(13):5457-5464.
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