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Sentiment Analysis of Tweets on Menu Labeling Regulations in the US

Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis...

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Autores principales: Yang, Yuyi, Lin, Nan, Batcheller, Quinlan, Zhou, Qianzi, Anderson, Jami, An, Ruopeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574510/
https://www.ncbi.nlm.nih.gov/pubmed/37836553
http://dx.doi.org/10.3390/nu15194269
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author Yang, Yuyi
Lin, Nan
Batcheller, Quinlan
Zhou, Qianzi
Anderson, Jami
An, Ruopeng
author_facet Yang, Yuyi
Lin, Nan
Batcheller, Quinlan
Zhou, Qianzi
Anderson, Jami
An, Ruopeng
author_sort Yang, Yuyi
collection PubMed
description Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news. Our temporal analysis revealed that tweeting peaked around major policy announcements, with a majority categorized as neutral or news-related. The prevalence of news tweets declined after 2017, as neutral views became more common over time. Deep neural network models like RoBERTa achieved strong performance (92% accuracy) in classifying sentiments. Key predictors of tweet sentiments identified by the random forest model included the author’s followers and tweeting activity. Despite limitations such as Twitter’s demographic biases, our analysis provides unique insights into the evolution of perceptions on the regulations since their inception, including the recent rise in negative sentiment. It underscores social media’s utility for continuously monitoring public attitudes to inform health policy development, execution, and refinement.
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spelling pubmed-105745102023-10-14 Sentiment Analysis of Tweets on Menu Labeling Regulations in the US Yang, Yuyi Lin, Nan Batcheller, Quinlan Zhou, Qianzi Anderson, Jami An, Ruopeng Nutrients Article Menu labeling regulations in the United States mandate chain restaurants to display calorie information for standard menu items, intending to facilitate healthy dietary choices and address obesity concerns. For this study, we utilized machine learning techniques to conduct a novel sentiment analysis of public opinions regarding menu labeling regulations, drawing on Twitter data from 2008 to 2022. Tweets were collected through a systematic search strategy and annotated as positive, negative, neutral, or news. Our temporal analysis revealed that tweeting peaked around major policy announcements, with a majority categorized as neutral or news-related. The prevalence of news tweets declined after 2017, as neutral views became more common over time. Deep neural network models like RoBERTa achieved strong performance (92% accuracy) in classifying sentiments. Key predictors of tweet sentiments identified by the random forest model included the author’s followers and tweeting activity. Despite limitations such as Twitter’s demographic biases, our analysis provides unique insights into the evolution of perceptions on the regulations since their inception, including the recent rise in negative sentiment. It underscores social media’s utility for continuously monitoring public attitudes to inform health policy development, execution, and refinement. MDPI 2023-10-06 /pmc/articles/PMC10574510/ /pubmed/37836553 http://dx.doi.org/10.3390/nu15194269 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Yuyi
Lin, Nan
Batcheller, Quinlan
Zhou, Qianzi
Anderson, Jami
An, Ruopeng
Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title_full Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title_fullStr Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title_full_unstemmed Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title_short Sentiment Analysis of Tweets on Menu Labeling Regulations in the US
title_sort sentiment analysis of tweets on menu labeling regulations in the us
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10574510/
https://www.ncbi.nlm.nih.gov/pubmed/37836553
http://dx.doi.org/10.3390/nu15194269
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