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Research Papers: Design Automation

A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank

[+] Author and Article Information
Dedy Suryadi

Enterprise Systems Optimization Laboratory,
Department of Industrial and Enterprise
Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801;
Industrial Engineering Department,
Parahyangan Catholic University,
Bandung 40141, Indonesia,
e-mails: suryadi2@illinois.edu;
dedy@unpar.ac.id

Harrison Kim

Enterprise Systems Optimization Laboratory,
Department of Industrial and Enterprise
Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: hmkim@illinois.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 20, 2017; final manuscript received July 9, 2018; published online September 18, 2018. Assoc. Editor: Scott Ferguson.

J. Mech. Des 140(12), 121403 (Sep 18, 2018) (12 pages) Paper No: MD-17-1779; doi: 10.1115/1.4040913 History: Received November 20, 2017; Revised July 09, 2018

In the buying decision process, online reviews become an important source of information. They become the basis of evaluating alternatives before making purchase decision. This paper proposes a methodology to reveal one of the hidden alternative evaluation processes by identifying the relation between the observable online customer reviews and sales rank. This methodology applies a combined approach of word embedding (word2vec) and X-means clustering, which produces product-feature words. It is followed by identifying sentiment words and their intensity, determining connection of words from dependency tree, and finally relating variables from the reviews to the sales rank of a product by a regression model. The methodology is applied to two data sets of wearable technology and laptop products. As implied by the high predicted R-squared values, the models are generalizable into new data sets. Among the interesting findings are the statements of problems or issues of a product are related to better sales rank, and many product features that are mentioned in the review title are significantly related to sales rank. For product designers, the significant variables in the regression models suggest the possible product features to be improved.

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Figures

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Fig. 1

Five-stage model of buying decision process

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Fig. 2

Relations between adjectives and nouns in: (a) a sentence without negation and (b) a sentence with negation

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Fig. 4

The flowchart of the proposed methodology

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Fig. 3

Skip-gram model (Source: [32])

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Fig. 6

Connecting adjective (JJ) to nouns in a sentence: (a) direct child, (b) direct parent, (c-1) no relations found, so the search continues to (c-2) and (c-3) by moving the JJ toward the root; (c-2) indirect parent; (c-3) indirect child

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Fig. 5

Word assignment into clusters: (a) before adjustment and (b) after adjustment

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Fig. 7

Conversion into regression variables

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