Automated Mapping of Product Features Mined From Online Customer Reviews to Engineering Product Characteristics

Kang, Sung Woo; Tucker, Conrad S.

doi:10.1115/detc2016-59772

Cited by 6 publications

(4 citation statements)

References 64 publications

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“…Recently, attempts have been made to extract user experience information from nontraditional sources of information including online product reviews. This information is then translated using data analytics in order to extract product features and quantitatively investigate product feature preferences [39][40][41][42][43]. In preference modeling, the mathematical techniques traditionally rely on linear mapping.…”

Section: Methods Tomentioning

confidence: 99%

Cyber-Empathic Design: A Data-Driven Framework for Product Design

Ghosh

Olewnik

Lewis

et al. 2017

Journal of Mechanical Design

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A critical task in product design is mapping information from consumer to design space. Currently, this process largely depends on designers identifying and mapping psychological and consumer level factors to engineered attributes. In this way, current methodologies lack provision to test a designer's cognitive reasoning and could introduce bias when mapping from consumer to design space. In addition, current dominant frameworks do not include user–product interaction data in design decision making, nor do they assist designers in understanding why a consumer has a particular perception about a product. This paper proposes a framework—cyber-empathic (CE) design—where user–product interaction data are acquired using embedded sensors. To gain insight into consumer perceptions relative to product features, a network of psychological constructs is utilized. Structural equation modeling (SEM) is used as the parameter estimation and hypothesis testing technique, making the framework falsifiable in nature. To demonstrate effectiveness of the framework, a case study of sensor-integrated shoes is presented, where two models are compared—one survey-only and one using the cyber-empathic framework model. Covariance-based SEM (CB-SEM) is used to estimate the parameters and the fit indices. It is shown that the cyber-empathic framework results in improved fit over a survey-only SEM. This work demonstrates how low-level user–product interaction data can be used to understand and model user perceptions in a way that can support falsifiable design inference.

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Section: Methods Tomentioning

confidence: 99%

Cyber-Empathic Design: A Data-Driven Framework for Product Design

Ghosh

Olewnik

Lewis

et al. 2017

Journal of Mechanical Design

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“…Their study results point out that positive reviews recommended hotels mainly because of intangible aspects such as the attitude of the staff, while negative reviews were from those who were dissatisfied with tangible aspects such as furnishings. Kang and Tucker (2016) used NLP techniques such as WordNet, PageRank and Latent Dirichlet allocation (LDA) to develop a supervised text mining process. The process is employed to extract PFs from product reviews.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment

Asadabadi

Saberi

Sadghiani

et al. 2022

JEIM

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PurposeThe purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.Design/methodology/approachThis paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.FindingsThe paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.Originality/valueOnline product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.

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“…To convert customer preferences into the importance of engineering characteristics, Kang and Tucker 35 used the latent Dirichlet allocation (LDA)-based method to extract the user-desired product features implied in reviews and the engineering characteristics hidden in the patent documents. Moreover, the PageRank algorithm 36 was used to match a customer requirement term to corresponding engineering characteristics. However, the topic vectors that are extracted by LDA are difficult to understand and difficult to use to directly represent a product feature.…”

Section: Related Workmentioning

confidence: 99%

A systematic estimation approach for the importance of engineering characteristics based on online reviews

Xing

Wang

Lin

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Online reviews are a new source for the valuable voice of customers. By identifying the customer’s opinion, designers can comprehend the important features of a product to satisfy customer demand, thus enhancing the market competitiveness of the product. Customers have opinions on multiple aspects of products hidden in reviews, and sentiment divergence may exist. Moreover, there is a gap between customer requirements and the product’s system requirements. How to effectively analyze a large number of reviews to extract the aspect-level customer opinion and thus determine the most important product engineering characteristics in design are the critical challenges for market-driven design. A systematic requirement analysis framework is proposed in this work. First, a convolutional neural network and sentiment analysis are used for opinion mining of online reviews. Then, based on fuzzy logic, the customer sentiment divergence (which is quantified by controversy indexes) and the average sentiment of a requirement are used to determine the degree of satisfaction. Finally, based on the product’s quality function development matrix, the satisfaction and frequency of the customer requirements are used to estimate the importance of the product’s engineering characteristics, which identifies the focus of product design. A case study of a hair dryer is given to demonstrate the effectiveness of the proposed methods.

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Automated Mapping of Product Features Mined From Online Customer Reviews to Engineering Product Characteristics

Cited by 6 publications

References 64 publications

Cyber-Empathic Design: A Data-Driven Framework for Product Design

Cyber-Empathic Design: A Data-Driven Framework for Product Design

Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment

A systematic estimation approach for the importance of engineering characteristics based on online reviews

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