| dc.contributor.advisor | Hauser, John R. | |
| dc.contributor.author | Mao, Chengfeng | |
| dc.date.accessioned | 2025-03-24T18:44:59Z | |
| dc.date.available | 2025-03-24T18:44:59Z | |
| dc.date.issued | 2025-02 | |
| dc.date.submitted | 2025-02-14T16:20:48.716Z | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/158810 | |
| 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.publisher | Massachusetts Institute of Technology | |
| dc.rights | In Copyright - Educational Use Permitted | |
| dc.rights | 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 | |
| dc.description.degree | S.M. | |
| dc.contributor.department | Sloan School of Management | |
| mit.thesis.degree | Master | |
| thesis.degree.name | Master of Science in Management Research | |
