| 基于NLPIR框架的食品安全网络舆情管理指标体系建构 |
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| 作者姓名： | 徐博 |
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| 作者单位： | 清华大学 新闻与传播学院, 北京 100084 |
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| 摘 要： | 在网络环境中,食品安全舆情事件具有快速传播和高影响力的特点。舆论问题如果不认真对待,不及时解决,就极有可能会导致突发性群体事件。食品安全网络舆情管理指标体系的构建有助于识别、评估、预测和应对公众舆论。对食品安全网上舆情事件传播机制做出了阐释；基于大数据的食品安全网络舆情分析系统架构,建立食品安全网络舆情指标体系,建构食品安全网络舆情预警模型；从政府、媒体和企业等方面,对大数据时代食品安全网络舆情预警工作提出解决对策,阐释了利用自然语言处理与信息检索(NLPIR)等技术手段,采集、理解、分析、提取并最终深入挖掘网络舆情信息,做到对舆情早发现、早预警、早处理、解民忧。公众喜欢通过网络表达情绪和发表个人言论,因此政府、媒体、公众和企业应将食品安全的舆情预警放在重要位置。及早发现、预警、处理和缓解网络舆情事件对政府和相关部门具有重大意义。
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| 关 键 词： | 食品安全 网络舆情 大数据 人工智能 自然语言处理与信息检索 |
| 收稿时间： | 2022/10/31 0:00:00 |
Constructing Online Public Sentiment Management Index System for Food Safety Utilizing NLPIR Framework |
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| Authors: | XU Bo |
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| Affiliation: | School of Journalism and Communication,Tsinghua University,Beijing 100084, China |
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| Abstract: | In the online environment, public sentiment events on food safety are characterized by their rapid disseminations and high impacts. If public sentiment issues were not taken seriously and addressed in a timely manner, they are highly likely to lead to sudden mass incidents. Constructing a robust online public sentiment management index system for food safety helps identify, evaluate, predict and respond to public opinion. This paper firstly explained the mechanism of food safety online public sentiment dissemination. Secondly, the technical architecture of food safety online public sentiment analysis system based on big data was used to build a food safety online public sentiment index system and establishes a food safety online public sentiment early warning model. Finally, solutions to early warning of online public sentiment on the topic of food safety in the era of big data from the government, media and enterprises aspects were proposed. The use of natural language processing and information retrieval (NLPIR) and other technical means to collect, understand, analyze, extract and ultimately dive deeper into information on public sentiment on the internet was explained, so that public sentiment could be detected, warned and dealt with early, and public concerns could be addressed. With the public''s preferences for expressing emotions and making personal statements via the internet, the government, the media, the public and companies should give priority to early warnings of public sentiment on food safety. How to detect, warn, handle and mitigate online public sentiment events early is of great significance to the government and relevant departments. |
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| Keywords: | food safety online public sentiment big data artificial intelligence NLPIR |
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