气候变化领域集成服务门户(CCPortal): Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

CCPortal

DOI	10.2196/47826
	Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study
	Molenaar, Annika; Lukose, Dickson; Brennan, Linda; Jenkins, Eva L.; Mccaffrey, Tracy A.
发表日期	2024
ISSN	1438-8871
起始页码	26
卷号	26
英文摘要	Background: Social media has the potential to be of great value in understanding patterns in public health using large-scale analysis approaches (eg, data science and natural language processing [NLP]), 2 of which have been used in public health: sentiment analysis and topic modeling; however, their use in the area of food security and public health nutrition is limited. Objective: This study aims to explore the potential use of NLP tools to gather insights from real -world social media data on the public health issue of food security. Methods: A search strategy for obtaining tweets was developed using food security terms. Tweets were collected using the Twitter application programming interface from January 1, 2019, to December 31, 2021, filtered for Australia -based users only. Sentiment analysis of the tweets was performed using the Valence Aware Dictionary and Sentiment Reasoner. Topic modeling exploring the content of tweets was conducted using latent Dirichlet allocation with BigML (BigML, Inc). Sentiment, topic, and engagement (the sum of likes, retweets, quotations, and replies) were compared across years. Results: In total, 38,070 tweets were collected from 14,880 Twitter users. Overall, the sentiment when discussing food security was positive, although this varied across the 3 years. Positive sentiment remained higher during the COVID-19 lockdown periods in Australia. The topic model contained 10 topics (in order from highest to lowest probability in the data set): Global production, Food insecurity and health, Use of food banks, Giving to food banks, Family poverty, Food relief provision, Global food insecurity, Climate change, Australian food insecurity, and Human rights. The topic Giving to food banks, which focused on support and donation, had the highest proportion of positive sentiment, and Global food insecurity, which covered food insecurity prevalence worldwide, had the highest proportion of negative sentiment. When compared with news, there were some events, such as COVID-19 support payment introduction and bushfires across Australia, that were associated with high periods of positive or negative sentiment. Topics related to food insecurity prevalence, poverty, and food relief in Australia were not consistently more prominent during the COVID-19 pandemic than before the pandemic. Negative tweets received substantially higher engagement across 2019 and 2020. There was no clear relationship between topics that were more likely to be positive or negative and have higher or lower engagement, indicating that the identified topics are discrete issues. Conclusions: In this study, we demonstrated the potential use of sentiment analysis and topic modeling to explore evolution in conversations on food security using social media data. Future use of NLP in food security requires the context of and interpretation by public health experts and the use of broader data sets, with the potential to track dimensions or events related to food security to inform evidence -based decision -making in this area.
英文关键词	food security; food insecurity; public health; sentiment analysis; topic modeling; natural language processing; infodemiology
语种	英语
WOS研究方向	Health Care Sciences & Services ; Medical Informatics
WOS类目	Health Care Sciences & Services ; Medical Informatics
WOS记录号	WOS:001198415100004
来源期刊	JOURNAL OF MEDICAL INTERNET RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/293865
作者单位	Monash University; Royal Melbourne Institute of Technology (RMIT); Monash University
推荐引用方式 GB/T 7714	Molenaar, Annika,Lukose, Dickson,Brennan, Linda,et al. Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study[J],2024,26.
APA	Molenaar, Annika,Lukose, Dickson,Brennan, Linda,Jenkins, Eva L.,&Mccaffrey, Tracy A..(2024).Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.JOURNAL OF MEDICAL INTERNET RESEARCH,26.
MLA	Molenaar, Annika,et al."Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study".JOURNAL OF MEDICAL INTERNET RESEARCH 26(2024).