Visualization of Customer Expectations from Web Text using

Co-occurrence Graph and Auto-labeling in the Service Market

Ryosuke Saga

1,2

, Naoaki Ohkusa

, Takafumi Yamashita

and Nahomi Maki

Dept. of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University,

1-1 Gakuen-cho, Naka-ku, Sakai, Japan

School of Knowledge and Information Systems, College of Sustainable System Sciences, Osaka Prefecture University,

1-1 Gakuen-cho, Naka-ku, Sakai, Japan

Dept. of Information Media, School of Informatics, Kanagawa Institute of Technology, 1030 Shimo-ogino, Atsugi, Japan

Keywords: Information Visualization, Service Science, Customer Expectations, Co-occurrence Graph, Clustering,

Auto-labeling.

Abstract: This study describes the visualization of customer expectations using the service science domain. Customer

expectations influence service quality and are considered important factors for user evaluation of services.

Customer expectations are constructed from word of mouth, rumors, and user experience. Investigation

using a questionnaire is useful in comprehending customer expectations, but this method is costly and time

consuming. In this research, we extract customer expectations from Web text consisting of massive word-

of-mouth data and visualize them using a co-occurrence graph. In addition, we apply clustering and auto-

labeling methods to easily understand the results of the co-occurrence graph. In the case study of a coffee

service, we are able extract topics related to customer expectations, but labeling methods are still subject to

improvement.

1 INTRODUCTION

Recently, the service industry has become dominant

in the world market and occupied more than 70% of

the gross domestic product in Japan. Additionally,

each real product has shifted toward the service

industry, thereby increasing the importance of

service.

Service science is a domain that systematically

understands services (Vargo et al., 2004; Maglio et

al., 2010; Maglio et al., 2006). Service science

examines concept of service beyond several

domains. It is well-known that service has the

feature called heterogeneity, which indicates that

quality and productivity of service are not stable.

Parasuraman et al. proposed a conceptual model

related to the quality of service (Parasuraman, 1985).

This conceptual model shows the gaps between

customer and service provider.

A significant concept called customer

expectation is included in this model. Customer

expectation refers to the perceived benefits a

customer expects before having the actual service

experience. Customer expectation consists of several

elements, including word of mouth, personal needs,

and past experience. The evaluation of the quality of

service comes under the influence of the gap

between customer expectation and feeling of

perceived service. If perceived service meets

customer expectations then customers give a high

evaluation; otherwise, customers’ evaluation is low.

Customer expectation leads to customer

satisfaction, and identifying customer expectation is

important to provide proper quality of service that

fills the gap between customer expectation and

perceived service. Investigation using a

questionnaire is useful in comprehending customer

expectations, but this method is costly and time

consuming.

The Web can now be used to check for a service

or a product from Google and Amazon.com

(Chakrabarti, 2003; Liu, 2008). Much useful word-

of-mouth and product/service information that can

build up customer expectations is found in these

Web pages and e-commerce sites. These Web pages

can be accessed by future customers who come

under the influence of word of mouth.

Saga R., Ohkusa N., Yamashita T. and Maki N.

Visualization of Customer Expectations from Web Text using Co-Occurrence Graph and Auto-labeling in the Service Market.

DOI: 10.5220/0006238303170321

In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), pages 317-321

ISBN: 978-989-758-228-8

317

Therefore, based on the assumption that

indications of customer expectations can be found

on the Web, the research question “Can we visualize

customer expectations from the Web?” is formulated.

We attempt to visualize customer expectations from

the Web to answer this question. In this visualization,

we use co-occurrence graphs in which nodes show

the keywords in Web pages and edges indicate the

relationship among keywords. In this study, we

target coffee service, which is one of the

representative service products because of its

heterogeneity that each person feels the different

taste for each coffee. Then, the co-occurrence graph

is utilized to show information, such as coffee

brands and tastes, to represent customer expectations.

Several topics can be found in the co-occurrence

graph, but customer expectation is difficut to

understand from the graph when the number of

nodes is large. We label each topic automatically to

use the machine learning method called auto-

labeling.

The contributions of this paper are as follows:

 The first approach is visualization of the

service science area. This approach provides

visualization results and helps the service

provider to easily comprehend the situation of

service usage and evaluation.

 Clustering and auto-labeling are combined to

understand the results of the co-occurrence

graphs. This approach is valuable to this

visualization domain and can be applied in

other co-occurrence visualizations.

2 VISUALIZATION PROCESS

The visualization process is explained in this

section. First, we extract keywords from Web pages

and utilize texts without illustrations or visualized

elements such as pictures and videos. However,

customer expectations are difficult to discover and

understand if the nodes appear in co-occurrence.

Clusters with dense edges show topics for clustering

the co-occurrence graph and adding labels

automatically.

2.1 Keyword Extraction and Edge

Extraction

We extract texts from Web pages related to services.

We use the term frequency-inverse document

frequency (TF-IDF) algorithm to extract the

keywords obtained from the top ranked words. The

TF-IDF algorithm is expressed as follows:



(1)

}:{

log

tdd

idf

∋

(2)

iiii

idftfidftf *=

(3)

In these formulas, n

is the appearance of word t

, Σ

is the sum of all of words, |D| is the number of

documents, and d is the number of documents

including the word t

We then extract the edges. This study presents

the edges using the Jaccard coefficient over a

threshold. The Jaccard coefficient is expressed as

()

AB AB

Jaccard A B

BBAB

∩∩

∪+−∩

(4)

where |A| is the number of documents with a word A

and |A∩B| is the number of documents with words

both A and B.

2.2 Clustering

Clustering is a method used to understand the

causality and topics hidden in the data and to

summarize, classify, and separate data. Several

clustering methods, also known as community

detection methods, are used for graph mining. In this

study, we use the method proposed by Newman

(Newman et al. 2004) that aggregates the nodes

based on modularity, which has a value close to 1.

Modularity is defined as follows:

Q =

-a

(5)

where e

is the percentage of the number of edges

from cluster i to cluster j to the total edges, e

is the

percentage of the number of internal edges in cluster

i to the total number of edges, and a

is the

percentage of the number of edges connected to

cluster i to the total number of edges. The Newman

method focuses on Δ, as shown in formula (6):

)(2

jiij

aaeQ −=Δ

)

The process of the Newman method is as follows:

1. Allocate all nodes as clusters.

2. Calculate Δ for all pairs of clusters.

3. Aggregate two clusters with the highest Δ.

4. Repeat steps 2–4 until Δ <0.

2.3 Auto-labeling for Clusters

After clustering, we attach the labels to each cluster.

We refer to the study of Mei et al. (2007) for the

IVAPP 2017 - International Conference on Information Visualization Theory and Applications

318

process. Here, we regard a label as the combination

between two words.

This method generates the label showing the

meaning of topics for the topic models. The topic

model is a statistical representation used to infer

topics based on the assumption that each document

shows and contains some topics. Given that the

concept of a label includes the concept of a topic,

Mei et al. (2007) estimated the relationships between

label and topic. The relationships are regarded as an

optimization problem with the minimization of

Kullback–Leibler divergence (Kullback et al., 1951)

between word distributions and the maximization of

mutual information between label and topic. The

relationships are based on the idea that a proper label

has the feature that determines the low meaning gap

between word and label.

2.3.1 Meaning Score

The meaning relationship between topic  and label

l is obtained by Score(l,), which is calculated by

the Kullback–Leibler divergence expressed as

formula (7):

() ( )

()



−=−=

lwp

wplDlScore

log|||),(

θθθ

()



−−=

Cwp

Clwp

Cwp

log|

θθ

()



−

lwp

Clwp

()

()()

() ()

()

,| |,

|log || |

|| |

w w

wl C p w lC

pw D C pw

wC plC pwl

θθθ

=−−



()( )( ) ()

ClBiasCDClwPMIwp

,|||,| +−=



θθ

(7)

where w is the word, l is the label,  is the topic, and

C is the text collection (or corpus), i.e., the texts

from the Web pages. PMI is the pointwise mutual

information, and (||) shows the Kullback–

Leibler divergence betweenand.

2.3.2 Labeling of the Cluster in the

Co-occurrence Graph

Using the concept presented in Sections 2.3.1 and

2.3.2, we label the clusters in the co-occurrence

graph. The labeling process is as follows:

1. Specify the documents from the extracted nodes

corresponding to the keywords of each cluster

in the co-occurrence graph. The specified

documents are initially regarded as those

belonging to a cluster based on the assumption

that specified documents (i.e., C in formula (7))

express the topic  of the cluster.

2. Generate labels L={l

, …, l

} from the

keywords in the documents. Each label consists

of multiple words.

3. D(||) and Bias(, ) are not related to the

score ranks and can be ignored because l and 

are generated from the same document

collection C, and D(||) is a constant value.

Finally, score (, ) is calculated using the

following formula:

() ( )

()

()()



lpwp

lwp

wplScore

log|,

θθ

(8)

2.3.3 Label Disambiguation

When several topics are found, each topic should

have a different label. To summarize only one topic

by a label, we modify the label score in formula (8)

into formula (9).



−

+≈

jii

lScore

...1

),(

),()

1(),(

(9)

where k is the number of topics and

is a parameter.

A label is attached to the most related topic using

this formula.

3 CASE STUDY

3.1 Environment and Dataset

We target the coffee market, which is one of the

representative services, as a case study of service

using top 200 results acquired from google.co.jp by

keyword “coffee”. During keyword extraction, we

regard nouns and adjectives as keywords. We extract

the top 500 keywords with high TF-IDF values

using the TF-IDF algorithm. Then, we extract the

edges using the Jaccard coefficient of over 0.3. The

label consists of two keywords, and the label

candidates are generated from the combination of

two keywords appearing in a cluster. We set the

parameter

to 0.7 in this labeling. Moreover, we ask

seven students to label each cluster from the

visualized co-occurrence graph. The labeled results

are called man-label.

3.2 Result

The result of the case study is illustrated in Figure 1

and Table 1. Figure 1 is illustrated by Kamada-

Kawai layout (Kamada et al. 1989) and shows the

clusters classified into different colors. One large

Visualization of Customer Expectations from Web Text using Co-Occurrence Graph and Auto-labeling in the Service Market

319

Figure 1: Visualization results from Web text about coffee service.

Table 1: System-generated labels and man-labels for each cluster.

component and other small components are also

presented. In the large component, we can find

seven clusters using the Newman method. Then, we

label the cluster that includes more than six nodes

because a topic of a cluster with only a few nodes

can be easily predicted.

As shown in Table 1, we can extract the cluster

for general customer expectation (e.g., clusters 1 and

5) indicating the coffee brand and the growing

coffee production area. However, a gap can be

observed between the results of the auto-label and

that of the man-label because the latter is based on

the co-occurrence graph and abstracted compared

with the former.

Finally, we conclude the case study.

 We extract the cluster like customer

expectation so that the visualization of

customer expectations is succeeded by using

web text and co-occurrence graph.

 Some labels by auto-labeling methods

succeeded for some clusters to understand the

clusters. However, there is room for

improvement of this method.

4 CONCLUSION

This study described the visualization of customer

expectations from Web text. For visualization, we

utilized the co-occurrence graph consisting of

keywords and co-occurrence relationships as nodes

and edges, respectively. We conducted clustering

and auto-labeling for each generated cluster to easily

ID Colour Auto Label Man Label

1 Robusta Item Rank, Production, Recipe

2 High Grade Effect, Efficacy

3 Phone Selling Store Selling, Sales

4Customer Set

The way of Drinking,

Preference, Place

5 Indonesia Robusta Brand, Producing Area

6 Cup Weather

Country, Condition of growing,

Lan d

7 Customer Update Limited time offer, limited sale

IVAPP 2017 - International Conference on Information Visualization Theory and Applications

320

comprehend the visualized graph.

In the case study, we visualized customer

expectations on coffee services. For clustering, we

used the Newman method and the auto-labeling

method proposed by Mei et al. Consequently, we

showed customer expectations using the co-

occurrence graph, but the result of auto-labeling

differed from that of man-labeling.

ACKNOWLEDGEMENTS

This research was supported by MEXT/JSPS

KAKENHI 25420448, 25240049, and 16K01250.

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