Food for thought: A natural language processing analysis of the 2020 Dietary Guidelines publice comments.

Contributing USMA Research Unit(s)

Center for Data Analysis and Statistics, Mathematical Sciences

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The American journal of clinical nutrition

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BACKGROUND: The Administrative Procedure Act of 1946 guarantees the public an opportunity to view and comment on the 2020 Dietary Guidelines as part of the policymaking process. In the past, public comments were submitted by postal mail or public hearings. The convenience of public comment through the Internet has generated increased comment volume, making manual analysis challenging.

OBJECTIVES: To apply natural language processing (NLP NLP is natural language processing.) to identify sentiment, emotion, and themes in the 2020 Dietary Guidelines public comments.

METHODS: Written comments to the Scientific Report of the 2020 Dietary Guidelines Advisory Committee that were uploaded and visible at https://beta.regulations.gov/docket/FNS-2020-0015 were extracted using a computer program and retained for analysis. All comments were filtered, and duplicates were removed. A 2-round latent Dirichlet analysis (LDA) was used to identify 3 overarching topics as well as subtopics addressed in the comments. Sentiment analysis was applied to categorize emotion and overall positive and negative sentiment within each topic.

RESULTS: Three different topics were identified by LDA. The first topic involved negative sentiment surrounding removing dairy from the guidelines because the commenters felt dairy is unnecessary. The second topic focused on positive sentiment involved in restricting added sugars. The third topic was too diverse to characterize under 1 theme. A second LDA within the third topic had 3 subtopics containing positive sentiment. The first subtopic valued the inclusion of dairy in the recommendations, the second involved the health benefits of consuming beef, and the third indicated that the recommendations lead to overall good health outcomes.

CONCLUSIONS: Public comments were diverse, held conflicting viewpoints, and often did not base comments on personal anecdotes or opinions without citing scientific evidence. Because the volume of public comments has grown dramatically, NLP has promise to assist in objective analysis of public comment input.

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