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Detecting reports of unsafe foods in consumer product reviews
OBJECTIVES: Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a ma...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951857/ https://www.ncbi.nlm.nih.gov/pubmed/31984365 http://dx.doi.org/10.1093/jamiaopen/ooz030 |
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author | Maharana, Adyasha Cai, Kunlin Hellerstein, Joseph Hswen, Yulin Munsell, Michael Staneva, Valentina Verma, Miki Vint, Cynthia Wijaya, Derry Nsoesie, Elaine O |
author_facet | Maharana, Adyasha Cai, Kunlin Hellerstein, Joseph Hswen, Yulin Munsell, Michael Staneva, Valentina Verma, Miki Vint, Cynthia Wijaya, Derry Nsoesie, Elaine O |
author_sort | Maharana, Adyasha |
collection | PubMed |
description | OBJECTIVES: Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. MATERIALS AND METHODS: We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. RESULTS: Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. DISCUSSION AND CONCLUSION: Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public. |
format | Online Article Text |
id | pubmed-6951857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-69518572020-01-24 Detecting reports of unsafe foods in consumer product reviews Maharana, Adyasha Cai, Kunlin Hellerstein, Joseph Hswen, Yulin Munsell, Michael Staneva, Valentina Verma, Miki Vint, Cynthia Wijaya, Derry Nsoesie, Elaine O JAMIA Open Research and Applications OBJECTIVES: Access to safe and nutritious food is essential for good health. However, food can become unsafe due to contamination with pathogens, chemicals or toxins, or mislabeling of allergens. Illness resulting from the consumption of unsafe foods is a global health problem. Here, we develop a machine learning approach for detecting reports of unsafe food products in consumer product reviews from Amazon.com. MATERIALS AND METHODS: We linked Amazon.com food product reviews to Food and Drug Administration (FDA) food recalls from 2012 to 2014 using text matching approaches in a PostGres relational database. We applied machine learning methods and over- and under-sampling methods to the linked data to automate the detection of reports of unsafe food products. RESULTS: Our data consisted of 1 297 156 product reviews from Amazon.com. Only 5149 (0.4%) were linked to recalled food products. Bidirectional Encoder Representation from Transformations performed best in identifying unsafe food reviews, achieving an F1 score, precision and recall of 0.74, 0.78, and 0.71, respectively. We also identified synonyms for terms associated with FDA recalls in more than 20 000 reviews, most of which were associated with nonrecalled products. This might suggest that many more products should have been recalled or investigated. DISCUSSION AND CONCLUSION: Challenges to improving food safety include, urbanization which has led to a longer food chain, underreporting of illness and difficulty in linking contaminated food to illness. Our approach can improve food safety by enabling early identification of unsafe foods which can lead to timely recall thereby limiting the health and economic impact on the public. Oxford University Press 2019-08-05 /pmc/articles/PMC6951857/ /pubmed/31984365 http://dx.doi.org/10.1093/jamiaopen/ooz030 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research and Applications Maharana, Adyasha Cai, Kunlin Hellerstein, Joseph Hswen, Yulin Munsell, Michael Staneva, Valentina Verma, Miki Vint, Cynthia Wijaya, Derry Nsoesie, Elaine O Detecting reports of unsafe foods in consumer product reviews |
title | Detecting reports of unsafe foods in consumer product reviews |
title_full | Detecting reports of unsafe foods in consumer product reviews |
title_fullStr | Detecting reports of unsafe foods in consumer product reviews |
title_full_unstemmed | Detecting reports of unsafe foods in consumer product reviews |
title_short | Detecting reports of unsafe foods in consumer product reviews |
title_sort | detecting reports of unsafe foods in consumer product reviews |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951857/ https://www.ncbi.nlm.nih.gov/pubmed/31984365 http://dx.doi.org/10.1093/jamiaopen/ooz030 |
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