{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/158810"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/158810","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Mining Multifaceted Customer Opinions from Online Reviews","abstract":"Online reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights.","abstract_html":"Online reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights.","abstract_has_math":false,"creators":["Mao, Chengfeng"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Hauser, John R."],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02","date_published":"2025-02","updated_at":"2026-07-22T22:22:07Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/158810","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Hauser, John R."]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management"]},{"key":"dc:creator","label":"Author","values":["Mao, Chengfeng"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-03-24T18:44:59Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-03-24T18:44:59Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Management Research"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/158810"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Online reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Mining Multifaceted Customer Opinions from Online Reviews"]}]}],"canonical_facts":{"dc:contributor.advisor":["Hauser, John R."],"dc:contributor.department":["Sloan School of Management"],"dc:creator":["Mao, Chengfeng"],"dc:date.accessioned":["2025-03-24T18:44:59Z"],"dc:date.available":["2025-03-24T18:44:59Z"],"dc:date.issued":["2025-02"],"dc:description.abstract":["Online reviews are a valuable source for studying customer needs and preferences. Previous studies focus on extracting a set of a priori defined constructs such as product attribute perception or explicit customer needs from reviews. Such a priori focus circumvents the limitations of certain natural language processing algorithms but discards valuable information in reviews that are not in the scope of the predefined construct. This study proposes a new method of extracting customer opinions and opinion targets from reviews with the Aspect Sentiment Triplet Extraction (ASTE) algorithm and then identifying theoretical constructs critical for product development with a posteriori interpretation method. We demonstrate the value of our proposed method by identifying granular opinion targets and expressions to find infrequent but important phenomena such as user innovations and delights."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/158810"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Mining Multifaceted Customer Opinions from Online Reviews"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Management Research"]},"updated_at":"2026-07-22T22:22:07Z"}