Mining Multifaceted Customer Opinions from Online Reviews
Name
Mao-maoc-SMMR-Management-2025-thesis.pdf
Description
Thesis PDF
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804.46 KB
Format
Adobe PDF
Checksum (MD5)
7e3b00d5075b67092e3abe6be70d895f
Author(s)
Mao, Chengfeng
Advisor(s)
Hauser, John R.
Date Issued
February 2025
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Sloan School of Management
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In Copyright - Educational Use Permitted
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