{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/116247"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/116247","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mining brand-related tweets for brand monitoring with consumer-based brand equity classification and sentiment analysis","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","abstract_has_math":false,"creators":["Yao, Jiachen"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Communications and Media","degree_department":null,"school":null,"contributors":["Sar, Sela","Yang, Feng","Yao, Mike","Su, Leona (Yi-Fan)","Ham, Chang-Dae"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-08","date_published":"2022-08","updated_at":"2026-07-22T22:24:55Z","subjects":["social media","brand equity","data analytics","BERT"],"languages":["en","eng"],"rights":["Copyright 2022 Jiachen Yao"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/116247","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sar, Sela","Yang, Feng","Yao, Mike","Su, Leona (Yi-Fan)","Ham, Chang-Dae"]},{"key":"dc:creator","label":"Author","values":["Yao, Jiachen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-08","2022-07-15"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Communications and Media"]},{"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 at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["social media","brand equity","data analytics","BERT"]}]},{"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 2022 Jiachen Yao"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/116247"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Jiachen Yao, accepted the attached license on 2022-07-14 at 14:14.","The student, Jiachen Yao, submitted this Dissertation for approval on 2022-07-14 at 14:22.","This Dissertation was approved for publication on 2022-07-15 at 14:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18311 on 2022-11-15 at 18:21:11","The growth of social media makes it possible for brand managers to monitor consumers’ perceptions and attitudes towards brand in a new way, which is timelier and more cost-efficient. The consumers’ responses to brand are also known as consumer-based brand equity (CBBE). Guided by brand equity literature, this dissertation provides a framework of how to analyze consumer-generated and brand-related textual data on Twitter effectively and insightfully with two studies, which involves qualitative content analysis and machine learning modelling. Study 1 focused on the development of the CBBE classification scheme. Along with brand equity literature, unsupervised topic modelling is conducted to aid the qualitative content discovery. Two coders then coded a sample of tweets as training data into one of the CBBE dimensions and meanwhile, they also labelled sentiment for the tweets. Study 2 utilized machine learning to help label data and answer research questions. Different natural language processing techniques and models are compared and summarized. It is found that the models with the Bidirectional Encoder Representations from Transformers (BERT) embedding technique applied have the best performance. Methodological implications are discussed. Besides, several use cases are provided in Study 2 to illustrate how the CBBE classification and sentiment analysis can be used together to generate consumer insights. Practical implications to brand and advertising are also discussed."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Mining brand-related tweets for brand monitoring with consumer-based brand equity classification and sentiment analysis"]}]}],"canonical_facts":{"dc:contributor":["Sar, Sela","Yang, Feng","Yao, Mike","Su, Leona (Yi-Fan)","Ham, Chang-Dae"],"dc:creator":["Yao, Jiachen"],"dc:date":["2022-08","2022-07-15"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms","The student, Jiachen Yao, accepted the attached license on 2022-07-14 at 14:14.","The student, Jiachen Yao, submitted this Dissertation for approval on 2022-07-14 at 14:22.","This Dissertation was approved for publication on 2022-07-15 at 14:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18311 on 2022-11-15 at 18:21:11","The growth of social media makes it possible for brand managers to monitor consumers’ perceptions and attitudes towards brand in a new way, which is timelier and more cost-efficient. The consumers’ responses to brand are also known as consumer-based brand equity (CBBE). Guided by brand equity literature, this dissertation provides a framework of how to analyze consumer-generated and brand-related textual data on Twitter effectively and insightfully with two studies, which involves qualitative content analysis and machine learning modelling. Study 1 focused on the development of the CBBE classification scheme. Along with brand equity literature, unsupervised topic modelling is conducted to aid the qualitative content discovery. Two coders then coded a sample of tweets as training data into one of the CBBE dimensions and meanwhile, they also labelled sentiment for the tweets. Study 2 utilized machine learning to help label data and answer research questions. Different natural language processing techniques and models are compared and summarized. It is found that the models with the Bidirectional Encoder Representations from Transformers (BERT) embedding technique applied have the best performance. Methodological implications are discussed. Besides, several use cases are provided in Study 2 to illustrate how the CBBE classification and sentiment analysis can be used together to generate consumer insights. Practical implications to brand and advertising are also discussed."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/116247"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Jiachen Yao"],"dc:subject":["social media","brand equity","data analytics","BERT"],"dc:title":["Mining brand-related tweets for brand monitoring with consumer-based brand equity classification and sentiment analysis"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Communications and Media"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:55Z"}