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Title:Improving the accuracy and diversity of feature extraction from online reviews using keyword embedding and two clustering methods
Author(s):Park, Seyoung
Advisor(s):Kim, Harrison M
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Online review, word embedding, word clustering, feature extraction
Abstract:In product design, it is essential to understand customer's requirements for product specifications. Traditional methods including surveys and interviews are still widely used to solve this problem, but with the increase of online channels such as Twitter and YouTube, customer opinions that can be collected online have increased exponentially. This online data has the advantage that it can be collected faster and cheaper than traditional surveys. Naturally, many studies have been conducted to analyze customer opinions on product design using online data. Among them, this thesis focused on the word embedding and clustering which is an automated feature extraction method using online product review data. The methodology does identify product features but has some limits. The research presented in this thesis addresses those limitations and proposes a new methodology to solve them. The improved results of the proposed methodology are demonstrated in case studies for three categories of products.
Issue Date:2020-05-01
Type:Thesis
URI:http://hdl.handle.net/2142/108292
Rights Information:Copyright 2020 Seyoung Park
Date Available in IDEALS:2020-08-27
Date Deposited:2020-05


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