Papers: User needs and preferences elicitation

Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews

[+] Author and Article Information
Feng Zhou

The George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Drive,
Atlanta, GA 30332
e-mail: fzhou35@gatech.edu

Roger Jianxin Jiao

The George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Drive,
Atlanta, GA 30332
e-mail: rjiao@gatech.edu

Julie S. Linsey

The George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
801 Ferst Drive,
Atlanta, GA 30332
e-mail: julie.linsey@me.gatech.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 28, 2014; final manuscript received February 4, 2015; published online May 19, 2015. Assoc. Editor: Wei Chen.

J. Mech. Des 137(7), 071401 (Jul 01, 2015) (12 pages) Paper No: MD-14-1525; doi: 10.1115/1.4030159 History: Received August 28, 2014; Revised February 04, 2015; Online May 19, 2015

Different from explicit customer needs that can be identified directly by analyzing raw data from the customers, latent customer needs are often implied in the semantics of use cases underlying customer needs information. Due to difficulties in understanding semantic implications associated with use cases, typical text mining-based methods can hardly identify latent customer needs, as opposite to keywords mining for explicit customer needs. This paper proposes a two-layer model for latent customer needs elicitation through use case reasoning. The first layer emphasizes sentiment analysis, aiming to identify explicit customer needs based on the product attributes and ordinary use cases extracted from online product reviews. Fuzzy support vector machines (SVMs) are developed to build sentiment prediction models based on a list of affective lexicons. The second layer is geared toward use case analogical reasoning, to identify implicit characteristics of latent customer needs by reasoning the semantic similarities and differences analogically between the ordinary and extraordinary use cases. Case-based reasoning (CBR) is utilized to perform case retrieval and case adaptation. A case study of Kindle Fire HD 7 in. tablet is developed to illustrate the potential and feasibility of the proposed method.

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Grahic Jump Location
Fig. 1

A two-layer model for latent customer needs elicitation through use case reasoning

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Fig. 2

Steps involved in latent customer needs elicitation

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Fig. 4

Customer opinions on individual product attributes

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Fig. 5

Customer opinions on attribute levels

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Fig. 6

Frequency of product attributes in customer reviews

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Fig. 7

Frequency of attribute levels

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Fig. 8

Extracted use cases from online user-generated product reviews (those percentages show the frequency of interaction elements)

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Fig. 9

Case representation of (1) an ordinary use case and (2) an extraordinary use case

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Fig. 10

Case retrieval pseudo algorithm



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