investigating the heterogeneity of product feature preferences … · 2018-02-07 · investigating...

http://www.engr.psu.edu/datalab/ 11

Investigating the Heterogeneity of Product

Feature Preferences Mined Using Online Product

Data Streams

Abhinav SinghDepartment of Industrial Engineering

The Pennsylvania State University

University Park, PA 16802

Email: [email protected]

Dr. Conrad TuckerEngineering Design and Department of Industrial Engineering

The Pennsylvania State University

University Park, PA 16802

Email: [email protected]

DETC2015- 47439


Overview

1. Research Motivation

2. Role of Online Product Reviews

3. Literature Review

4. Limitations

5. Research Hypothesis

6. Correlation between Product Performance and Reviews

7. Research Methodology

8. Case Study

9. Conclusion

10.Future Work


A Framework for Decision based Engineering Design, G.A.Hazelrigg,1998

Research Motivation

Demand = f(product , price , time)

“Optimize Price while Maximizing Utility”


Role of Online Product Reviews

Zhu & Zhang,2010


Literature Review

Literature Review


Product Attributes Acquisition

Researchers Conclusion Drawn

(P.L.Josty , 1986)

(R.Rothwell , 1992)

product developed according to customers’ needs are more likely to

succeed than those that are an outcome of new technological innovations

(Wassenaar et al. , 2004) proposed a Discrete Choice Analysis (DCA) approach, in combination

with Kano Method for product design and demand modeling in Decision

Based Design

(Trappey et al. 2009) Clustering Analysis as a tool for market segmentation based on

preferences, demographics and other such attributes to provide services in

accordance with customers’ expectations

(D. Dawson and R. G. Askin ,

1999)

(H. B. C. K. Kwong , 2002)

captured the requirements of customers to be used for Quality Function

Deployment(QFD), while designing new products using Fuzzy AHP

approaches

Literature Review


Mining Product Attributes


( S. Asur B. A. Huberman ,

2013 ) and

online customer review data can serve as strong indicators of

future outcomes

(C. S. Tucker and H. M. Kim

, 2008)

online customer survey data and Naïve Bayes Model for predictive

analysis to translate customers’ requirements into product design

targets

(Jin et al. , 2011) document profile model that extends the Apriori algorithm in

combination with Part-of-Speech Tagger, to enable designers to

design products centered to customer preferences

(Dave et al. , 2003) classified customer reviews as positive or negative in order to

quantify the importance of a feature in a product.

Literature Review



(G.A. Hazelrigg ,

1999)

as the number of individuals in a group increase, the number of

alternatives for a product’s design increases based on the aggregated

preferences of customers. Such a situation gives rise to irrationality in

design and thus a Customer-Centered view of design is not possible

(Besharati et al. ,

2002)

techniques allow researchers to aggregate individuals’ preferences and

design products in accordance to such preferences, it violates the AIT

and are not recommended

(Penchas 2002) It is impossible to translate 1000 individual preferences or priorities to a

social order and feed into a single system.

(R. Decker and M.

Trusov , 2010)

developed an econometric framework to turn individual preferences

from the opinions expressed online to aggregate preferences for product

development and improvement processes

(G.A. Hazelrigg ,

1996)

approaches like QFD and Total Quality Management (TQM) for such

aggregated preferences, can lead to erroneous results and are logically

inconsistent

Design Axioms in

Product Attribute Modeling

Literature Review


Limitations

• Heterogeneity of product attributes increase

complexity of product attribute optimization

• Challenges are exacerbated as customer is

unknown

• Uncertainty in predicting demand


Research Hypothesis

• H1:“there exists at least one product attribute that has a

positive sentiment across all customers” (i.e., must have).

• H2: “there exists at least one product attribute that has a

negative sentiment across all customers” (i.e., deal

breakers”).

• Null hypothesis : there are no product attributes with

homogeneous sentiments across customers.

Research Hypothesis


Correlation between iPhone 4 sale prediction and online product feedback

Tuarob & Tucker, 2013, Tuarob & Tucker,2015

Correlation between Product Demand

and Online Product Feedback


Research Methodology

Stop Word Removal

POS-Tagging

Stemming

Sentiment Analysis

Natural Language

Processing

Expectation

Maximization

Analysis of Sentiment

Scores across features

Methodology


Step 1 : Feature Space Creation

• Manufacturer’s description of

product obtained from

Wikipedia and product’s official

website.

Methodology


POS Tagging - Manufacturer’s Description

Relevance Ranking of Nouns as attributes

Step 1: Part Of Speech Tagging and Relevance Ranking

• Classification of text as Nouns, Noun

Phrases, Verbs etc.• Importance of

Nouns/Feature

s in the text

corpus

Methodology


Camera

AlarmBattery iOS

Speaker SIRI

Wi-Fi

“I hate when my

iPhone battery dies

within hours”

“Camera of my

iPhone is so so

so gooood”

Step 1 : Mapping Reviews to Product Features

• Opinions expressed by customer through online social media messages

are mapped to the features obtained in the feature space.

• Multiple features can be mapped from one statement for certain

reviews.

Methodology


Step 2 :Obtaining Sentiment Scores for Keywords

• Sentiment Analysis of review statements.

• Classification of sentiments into positive and negative based on the

sentiment expressed in the review by customer.

Methodology


Step 3 :Classification into

“Must Have” and “Deal Breaker” Features

Feature 1 Feature 2 Feature 3

Customer 1

Customer 2

.

.

.

.

Customer n

Classification X X X

Feature Classification Window

Feature Classification Window helps in

determining the class of a feature

depending on the Sentiment Score ‘s’

for an instance, mean of the sentiment

scores across all the customers and one

standard deviation ‘ϵ’ from the mean.

?

?

?

?

?

?

Methodology


Step 3 :Approximation of Missing Values

• Expectation Maximization Algorithm:

1. E – expected complete data likelihood ratio

2. M – maximize likelihood in E

E M

Methodology


Step 4 :Classification into

“Must Have” and “Deal Breaker” Features

Feature 1 Feature 2 Feature 3

Customer 1

Customer 2

.

.

.

.

Customer n

Classification Must Have Unclassified Deal Breaker

Methodology


Ambiguous Nature of Product Attributes

• Possibilities in such a case:

1. Attributes are assumed to be obsolete because they are

undesirable in the market (eg. Lead based products).

2. Attributes are assumed to be included in the next generation

design as they are desirable in the market (eg. Seat Belt in Car).

3. Customers are unaware of existence of attributes in the market

(eg. Bendable phone screens that are at their nascent stage).


Case Study

Missin

g Sen

timen

t Scores

0.59 1.23 0.65

0.56 2 1.36

2.6 0.26 -0.65

Data Source : 100 reviews

pertaining to iPhone 5 and

Manufacturer’s Product

Description

Sentiment Analysis:

Alchemy API

Approximation of Missing Values

:Expectation Maximization

Analyzing Positive and Negative Scores

Deal Breaker

Identification of Must Have and

Deal Breaker Features

---- ---- ------- -- - - -- -

Case Study


Case Study

Sample statements:

1. “iPhone 5 battery is supposed last longer than iPhone 4 but its

worse.”

2. “Siri. Not so good. Headphone Jack on bottom. Awful.”

3. “iOS on iPhone 5 is smooth and fast. I’m loving it.”

Review Battery Siri iOS Camera

1 0.59 0.74 -0.23

2 -0.72

3 0.13 -0.34 0.6 0.53

Review Battery Siri iOS Camera

1 0.59 -0.42 0.74 -0.23

2 0.63 -0.72 0.57 0.44

3 0.13 -0.34 0.6 0.53

Case Study


Classification into

“Must Have” and “Deal Breaker” Attributes

1 Battery 11 Cord Length

2 Charger 12 Earphones

3 iOS 13 Processor

4 Phone Case 14 Front Camera

5 Weight 15 Speaker

6 Camera 16 Connectivity

7 Siri 17 Headphone Jack

8 Price 18 Screen

9 Size 19 Alarm

10 Screenshot 20 Wi-Fi

Extracted iPhone 5 Attributes

Must Have Deal Breakers Unclassified

Light Weight Battery Speaker Camera

iOS Screen Processor Charger

Wi-Fi Screenshot Siri

Alarm Cord Length Front Camera

Phone Case Earphones Size

Price Connectivity

Headphone

Jack

Final Attribute Classification

Case Study


Conclusion

• Aggregating Product Attributes while designing a product

violates the design axioms.

• Discovery of homogenous product attributes across all

customers provides relevant information to the designers.

• Improving current state of a product based on such analysis

will alleviate the risk of product failure in the market


Future Work

• Testing methodology on a larger data set

• Need automatic identification of implicit

messages

• Developing robust missing value

approximation approach


ReferencesContributors: Abhinav Singh, Dr. Conrad TuckerAcknowledgement : Suppawong Tuarob

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investigating the heterogeneity of product feature preferences … · 2018-02-07 · investigating...

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