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使用自然语言处理来探索社交媒体对粮食安全的看法:情绪分析和主题建模研究
Annika Molenaar1, Dickson Lukose2, Linda Brennan3
1Department of Nutrition, Dietetics and Food, Monash University, Notting Hill, Australia.
Journal of medical Internet research
|March 21, 2024
概括
使用自然语言处理 (NLP) 的社交媒体分析揭示了公众对粮食安全的情绪. 像情感分析和主题建模这样的NLP工具可以跟踪食品安全对话,告知公共卫生决策.
科学领域:
- 公共卫生 营养 公共卫生 营养
- 数据科学数据科学数据科学
- 计算社会科学 计算社会科学
背景情况:
- 社交媒体为公共卫生洞察提供了有价值的大规模数据.
- 自然语言处理 (NLP) 工具,如情绪分析和主题建模,在粮食安全研究中未得到充分利用.
- 了解关于粮食安全的公共话语对于公共健康营养至关重要.
研究的目的:
- 探索NLP工具对分析社交媒体数据对粮食安全的有用性.
- 识别社会媒体上讨论的与粮食安全有关的公众情绪和关键话题.
- 评估粮食安全对话随着时间的推移的演变.
主要方法:
- 从澳大利亚用户 (2019-2021) 收集了与食品安全相关的38,070条推文.
- 应用情感分析 (瓦伦斯意识到词典和情感推理器) 和主题建模 (隐藏的迪里克莱特分配).
- 比较的情绪,主题和参与 (喜欢,转发,回复) 跨年和与COVID-19等事件有关的事件.
主要成果:
- 总体来说,对粮食安全的积极情绪,随着多年的变化和COVID-19封锁期间更高的积极性.
- 确定了10个关键主题,包括"粮食不安全和健康"",食品银行的使用"和"澳大利亚的粮食不安全".
- 负面的推特获得了更高的参与度;"向食品银行捐款"是最积极的情绪,而"全球粮食不安全"是最负面的情绪.
结论:
- 情绪分析和话题建模有效地通过社交媒体跟踪食品安全对话的演变.
- 未来的应用需要公共卫生专家的解释和更广泛的数据集,以基于证据的决策.
- NLP提供了一个强大的工具,用于监测公共卫生营养问题,如粮食安全.
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