An Automatic Classification Approach
to Business Stakeholder Analysis on
the Web
Wingyan Chung, Hsinchun Chen,
Edna O. F. Reid
January 16, 2003
Agenda
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Introduction
Literature Review
Research Questions
Research Approach and Testbed
Evaluation Methodology
Experimental Results and Discussion
Conclusions and Future Directions
2
Introduction
Current Business Environment
• Networked business environment facilitates
information sharing
• Collaborative commerce integrates business
processes among partners through electronic
sharing of information
– Sales support, vendor management, planning and
scheduling, demand planning, etc.
• Knowledge sharing about stakeholder
relationships through a company’s Web sites
and pages
– Textual content or annotated hyperlinks
4
Problems
• Information overload on the Web
– Hinders analysis of stakeholder relationships
• Knowledge hidden in interconnected Web
resources
– Posing challenges to identifying and classifying
various business stakeholders
• e.g., A company’s manager may not know who are using
their company’s Web resources
– Problem of traditional stakeholder analysis
– The emergence of electronic commerce
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An Automatic Classification
Approach
• Need better approaches to uncovering such
knowledge
– Enhance understanding of business stakeholders
– Enhance understanding of competitive
environments
• We propose an automatic classification
approach to business stakeholder analysis
– Human knowledge + machine-learned information
• We will review related areas in stakeholder
analysis and Web page classification
techniques
6
Literature Review
Stakeholder Analysis
• Stakeholder theories evolve over time while
the view of firm changes
– Production view (19th century): Suppliers and
Customers
– Managerial view (20th century): + Owners,
Employees
– Stakeholder view (1960-80s) (Freeman, 1984): +
Competitors, Governments, News Media,
Environmentalists, …
– E-commerce view (1990s - now): + International
partners, Online communities, Multinational
employees, …
8
Summary of stakeholder types
Research
Stakeholder Types
Reid, 2003
Partners/suppliers, customer, employee, investor,
education institutions, media, portal, public, recruiter,
reviewer, competitor, unknown
Elias &
Cavana, 2000
Owners, community, unions, employees, government,
consumer advocates, competitors, financial community,
media, customers, SIG, suppliers
Agle et al.,
1999
Shareholders, employees, customers, government,
communities
Donaldson &
Preston, 1995
Investors, government, suppliers, trade associations,
employees, communities, customers, political groups
Clarkson, 1995 Employees, shareholders, customers, suppliers, public
stakeholders
• These types, ordered by their relevance to those
appearing on the Web, are important for practical
understanding of stakeholders of firms
9
Comparing Stakeholder Types* Used
Research
P E C S U M G R V O T F I N
Reid, 2003
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Elias &
Cavana, 2000
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Agle et al.,
1999
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Donaldson &
Preston, 1995
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Clarkson, 1995
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* P = Partners/suppliers, E = Employees/Unions, C = Customers,
S = Shareholders/investors, U = Education/research institutions,
M=Media/Portals,
G = Public/government, R = Recruiters, V = Reviewers, O = Competitors,
T = Trade associations, F = Financial institutions, I = Political groups,
N = SIG/Communities
(Note that a class “Unknown” is not included here)
10
Comments on Stakeholder
Research
• Strong explanatory power but are weak at
practical classification of stakeholders
• Conclusions drawn from old data
• Previous research rarely considers the many
opportunities offered by the Web for
stakeholder analysis, e.g.,
– Business intelligence, which is obtained from the
business environment, is likely to help in
stakeholder activities
– Tools have been developed to exploit business
intelligence but not yet applied to stakeholder
analysis
11
BI and Stakeholder Analysis
• Advanced BI tools often rely on Web mining
techniques to discover patterns on the Web
automatically (Etzioni 1996; Kosala & Blockeel 2000), e.g.,
– PageRank (Brin & Page 1998), HITS (Kleinberg
1999), Web IF (Ingwersen 1998)
– External links mirror social communication
phenomena (e.g., stakeholder relationships)
• Tools and approaches exploit Web content
and link structure information
– Ong et al 2001; Tan et al. 2002; Reiterer et al.
2000; Chung et al. 2003; Reid 2003; Byrne 2003
12
Information on the Web
• Structural and textual content
• But commercial BI tools lack analysis
capability (Fuld et al. 2002)
• Need to automate stakeholder
classification, a primary step in
stakeholder analysis
– Automatic classification of Web pages is a
promising way to alleviate the problem
13
Web Page Classification
• The process of assigning pages to predefined
categories
– Helps to discover companies’ stakeholders on the
Web and enables companies to understand the
competitive environment better
• Major approaches include k-nearest neighbor,
neural network, Support Vector Machines,
and Naïve Bayesian network (Chen & Chau 2004)
• Previous work
– Kwon and Lee 2003; Mladenic 1998; Furnkranz
1999; Lee et al. 2002; Glover et al. 2002
14
Feature selection in Web Page
Classification
• Features considered
– Page textual content: full text, page title, headings
– Link related textual content: anchor text, extended
anchor text, URL strings
– Page structural information: #words, #page outlinks, inbound outlinks (i.e., links that point to its
own company), outbound outlinks (i.e., links that
point to external Web site)
• Methods for selection
– Human judgment / Use of domain lexicon
– Feature ratios and thresholding
– Frequency counting / MI
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Research Questions
Research Gaps
• Stakeholder research provides rich theoretical
background but rarely considers the
tremendous opportunities offered by the Web
for stakeholder analysis
– Conclusions drawn from old data may not reflect
rapid development in e-commerce
• Existing BI tools lack stakeholder analysis
capability
• Automatic Web page classification techniques
are well developed but have not yet been
applied to business stakeholder classification
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Research Questions
• How can we develop an automated approach
to business stakeholder analysis on the Web?
• How can Web page textual content and
structural information be used in such an
approach?
• What are the effectiveness (measured by
accuracy) and efficiency (measured by time
requirement) of such an approach for
business stakeholder classification on the
Web?
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Research Approach and
Testbed
Automatic Classification Approach
• Purpose: To automatically classify the stakeholders of
businesses on the Web in order to facilitate
stakeholder analysis
• Rationale
– Business stakeholders should have identifiable clues that can
be used to distinguish their types
– The Web content and structural information is important for
understanding the clues for stakeholder classification
• Two generic steps:
– Creation of a domain lexicon that contains key textual
attributes for identifying stakeholders
– Automatic classification of Web pages (stakeholders) linking
to selected companies based on textual and structural
content of Web pages
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Building a Research Testbed
• Business stakeholders of the KM World top
100 KM companies (McKellar 2003)
• Used backlink search function of the Google
search engine to search for Web pages
having hyperlinks pointing to the companies’
Web sites
• For each host company, we considered only
the first 100 results returned
– Removed self links and extra links from same sites
– After filtering, we obtained 3,713 results in total
– Randomly selected the results of 9 companies as
training examples (414  283 pages stored in DB)
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Creation of a Domain Lexicon
• Manually read through all the Web pages of the nine
companies’ business stakeholders to identify one-,
two-, and three-word terms that were indicative of
business stakeholder types
• Extracted a total of 329 terms (67 one-word terms,
84 two-word terms, and 178 three-word terms), e.g.,
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Automatic Stakeholder
Classification
• Three steps:
Manual
Tagging
Feature
selection
Automatic
classification
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Manual
tagging
Feature
selection
Automatic
classification
Manual Tagging
• Manually classified each of the stakeholder pages of
the nine selected companies into one of the 11
stakeholder types (based on our review on slides 9-10)
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Manual
tagging
Feature
selection
Automatic
classification
Feature Selection
• Structural content features: binary variables
indicating whether certain lexicon terms are
present in the structural content
– A term could be a one-, two-, or three-word long
– Considered occurrences in title, extended anchor
text, and full text
• Textual content features: frequencies of
occurrences of the extracted features
– The first set of features was selected based on
human knowledge, while the second was selected
based on statistical aggregation, thereby
combining both kinds of knowledge
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An Example
(a media type)
Link to the
host company
(ClearForest)
<html>
<head>
<meta http-equiv="Content-Type" content="text/html;
charset=iso-8859-1" />
HTML hyperlink
and extended
anchor text
<title>David Schatsky: Search and Discovery in the Post-Cold
War Era</title> ...
<p>I just saw a demo by <a href =
"http://www.clearforest.com"> ClearForest, </a> a company
that provides tools for analyzing unstructured textual
information. It's truly amazing, and truly the search tool
for the post-Cold War era. ... </p> ...
</body>
</html>
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Manual
tagging
Feature
selection
Automatic
classification
Automatic Classification
• A feedforward/backpropagation neural
network (Lippman 1987) and SVM (Joachims,
1998) were used due to their robustness in
automatic classification
– Train the algorithms using the stakeholder pages
of the 9 training companies and obtain a model or
sets of weights for classification
– Test the algorithms on sets of stakeholder pages
of 10 companies different from training examples
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Evaluation Methodology
Experimental Design
• Consisted of algorithm comparison, feature
comparison, and a user evaluation study
– Compared the performance of neural network
(NN), SVM, baseline method (random
classification), human judgment
– Compared structural content features, textual
content features, and a combination of the two
sets of features
– 36 Univ of Arizona business students performed
manual stakeholder classification and provided
comments on the approach
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Performance Measures
• Effectiveness:
– Overall accuracy
– Within-class accuracy
• Efficiency: time used (in minutes)
• User subjective ratings and comments
User Study
• Each subject was introduced to stakeholder
analysis and was asked to use our system
named “Business Stakeholder Analyzer (BSA)”
to browse companies’ stakeholder lists
• We randomly selected three companies
(Intelliseek, Siebel, and WebMethods) from
testing companies to be the targets of
analysis
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Hypotheses (1)
• H1: NN and SVM would achieve similar
effectiveness when the same set of
features was used
– Both techniques were robust
– Procedure: created 30 sets of stakeholder
pages by randomly selecting groups of 5
stakeholder pages of each of the 10 testing
companies
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Hypotheses (2)
• H2: NN and SVM would perform better than
the baseline method
– Incorporated human knowledge and machine
learning capability into the classification
• H3: Human judgment in stakeholder
classification would achieve effectiveness
similar to that of machine learning, but that
the former is less efficient
– They could make use of the Web page’s textual
and structural content in classifying stakeholders
– Humans might spend more time on it
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Hypotheses (3)
• H4 & H5 examined the use of different
types of features in automatic
stakeholder classification
– H4: structural = textual
– H5: combined > structural or textual alone
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Experimental Results and
Discussion
Algorithm Comparison
• H1 not confirmed
• NN performed significantly differently than
SVM when the same set of features was used
– NN performed significantly better than SVM when
structural content features were used
– SVM performed significantly better than NN when
textual content features or a combination of both
feature sets were used
– More studies would be needed to identify optimal
feature sets for each algorithm
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Effectiveness of the Approach
• H2 confirmed
• The use of any combination of features and
techniques in automatic stakeholder
classification outperformed the baseline
method significantly
– Our approach has integrated human knowledge
with machine-learned information related to
stakeholder types …
– and was significantly better than a random
conjecture
37
Comparing with Human
Judgment
• H3b and H3d (efficiency) confirmed
– Human: 22 minutes (average), varied
– Algorithms: 1 – 30 seconds (average)
– Showing high efficiency of using the automatic
approach to facilitate stakeholder analysis
• H3a and H3c (effectiveness) not confirmed
– Humans were significantly more effective than NN
or SVM
– They could rely on more clues in performing
classification
– Experience in Internet browsing and searching
helped narrow down choices
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However, the algorithms achieved better
within-class accuracies than humans in
frequently occurring types …
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Use of Features
• To our surprise, hypotheses H4a-b, H5a-b,
and H5d were not confirmed
– Different feature sets yielded different
performances of the algorithms
• Structural features enabled NN to achieve better
effectiveness than textual ones
• Textual and combined features enabled SVM to achieve
better effectiveness than structural ones
– Do not know exactly why
– Future research: studying the effect of features
and the nature of algorithms
• H5c was confirmed: structural content feature
did not add value to the performance of SVM
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Subjects’ Comments
• Overwhelmingly positive
• “It would be very helpful!”
• “That’s cool!”
• “I want to use it.”
Conclusions and Future
Directions
Conclusions
• Proposed an automatic classification
approach to business stakeholder analysis on
the Web
– Integrated Human expert knowledge + machinelearned information
– Promising in terms of effectiveness and efficiency
• A strong potential to use the approach to
augment traditional stakeholder classification
• Could potentially facilitate business analysts’
interaction with automated stakeholder
analysis systems in today’s networked
enterprises
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Future Directions
• To automate the next steps of business
stakeholder analysis
– With more expert participation and more Web
page data
• Type-specific stakeholder analysis
– e.g., partner relationships are often important in
developing business strategies
• Automating cross-regional business
stakeholder analysis
– Study multinational business partnerships and
cooperation and related HCI issues
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