{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129722"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129722","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Using text mining and other big data analytics to analyze public discussion of novel food technologies, using cultured meat as an example","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Chen, Tianli"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Food Science & Human Nutrition","degree_department":null,"school":null,"contributors":["Su, Leona Yi-Fan","Schmidt, Shelly J","Stasiewicz, Matthew Jon","Ng, Margaret Yee Man","Wang, Yi-Cheng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-05","date_published":"2025-05","updated_at":"2026-07-22T22:25:05Z","subjects":["Nomenclature","Public perceptions","Twitter","Generative large language models"],"languages":["en","eng"],"rights":["Copyright 2025 Tianli Chen"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129722","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Su, Leona Yi-Fan","Schmidt, Shelly J","Stasiewicz, Matthew Jon","Ng, Margaret Yee Man","Wang, Yi-Cheng"]},{"key":"dc:creator","label":"Author","values":["Chen, Tianli"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-05","2025-04-25"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Food Science & Human Nutrition"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Nomenclature","Public perceptions","Twitter","Generative large language models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Tianli Chen"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129722"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Tianli Chen, accepted the attached license on 2025-04-24 at 13:39.","The student, Tianli Chen, submitted this Dissertation for approval on 2025-04-24 at 14:12.","This Dissertation was approved for publication on 2025-04-25 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21956 on 2025-10-19 at 19:54:01","Understanding public opinions is crucial for the food industry and related regulatory bodies. Scholars, companies, and regulatory bodies have explored public opinion on food-related issues, including food products/technologies and food safety outbreaks, through various methods such as surveys and calls for comments. However, these methods can be limited by high costs and low response volumes, potentially leading to inaccurate data and hindering informed decision-making. Conversely, the vast amounts of online text data provide a rich, yet underutilized, source of public opinion that could greatly enhance decision-making in the food industry and among regulatory bodies. To address this gap, this work aims to leverage conventional big data analytics and develop new text mining tools to provide stakeholders with rapid and reliable insights into the evolving public discussion on food-related issues, empowering them to take immediate actions and make more informed decisions. This study uses cultured meat as a case study to gain comprehensive insights into the longitudinal public discussions of novel food technologies. It will first analyze the usage frequency, information dissemination, and topics associated with different names for cultured meat in Twitter discussions through volume analysis, network analysis, and word cloud analysis. Subsequently, the study will examine public attitudes toward cultured meat by conducting sentiment analysis on cultured meat-related tweets, leveraging a novel generative large language model (LLM)-based method. This study contributes by (1) providing a reference for the use of various analytical methods on online food-related data, (2) offering insights to cultured meat industry to support more informed decision-making, and (3) proposing a novel generative LLM-based text analysis method, which has the potential to overcome the time and cost limitations of traditional machine learning models."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using text mining and other big data analytics to analyze public discussion of novel food technologies, using cultured meat as an example"]}]}],"canonical_facts":{"dc:contributor":["Su, Leona Yi-Fan","Schmidt, Shelly J","Stasiewicz, Matthew Jon","Ng, Margaret Yee Man","Wang, Yi-Cheng"],"dc:creator":["Chen, Tianli"],"dc:date":["2025-05","2025-04-25"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01","The student, Tianli Chen, accepted the attached license on 2025-04-24 at 13:39.","The student, Tianli Chen, submitted this Dissertation for approval on 2025-04-24 at 14:12.","This Dissertation was approved for publication on 2025-04-25 at 16:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21956 on 2025-10-19 at 19:54:01","Understanding public opinions is crucial for the food industry and related regulatory bodies. Scholars, companies, and regulatory bodies have explored public opinion on food-related issues, including food products/technologies and food safety outbreaks, through various methods such as surveys and calls for comments. However, these methods can be limited by high costs and low response volumes, potentially leading to inaccurate data and hindering informed decision-making. Conversely, the vast amounts of online text data provide a rich, yet underutilized, source of public opinion that could greatly enhance decision-making in the food industry and among regulatory bodies. To address this gap, this work aims to leverage conventional big data analytics and develop new text mining tools to provide stakeholders with rapid and reliable insights into the evolving public discussion on food-related issues, empowering them to take immediate actions and make more informed decisions. This study uses cultured meat as a case study to gain comprehensive insights into the longitudinal public discussions of novel food technologies. It will first analyze the usage frequency, information dissemination, and topics associated with different names for cultured meat in Twitter discussions through volume analysis, network analysis, and word cloud analysis. Subsequently, the study will examine public attitudes toward cultured meat by conducting sentiment analysis on cultured meat-related tweets, leveraging a novel generative large language model (LLM)-based method. This study contributes by (1) providing a reference for the use of various analytical methods on online food-related data, (2) offering insights to cultured meat industry to support more informed decision-making, and (3) proposing a novel generative LLM-based text analysis method, which has the potential to overcome the time and cost limitations of traditional machine learning models."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129722"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Tianli Chen"],"dc:subject":["Nomenclature","Public perceptions","Twitter","Generative large language models"],"dc:title":["Using text mining and other big data analytics to analyze public discussion of novel food technologies, using cultured meat as an example"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Food Science & Human Nutrition"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}