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An Automatic Classification Approach to Business Stakeholder Analysis on the Web : An Automatic Classification Approach to Business Stakeholder Analysis on the Web Wingyan Chung, Hsinchun Chen, Edna O. F. Reid January 16, 2003


Agenda : Agenda Introduction Literature Review Research Questions Research Approach and Testbed Evaluation Methodology Experimental Results and Discussion Conclusions and Future Directions


Introduction : Introduction


Current Business Environment : 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


Problems : 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


An Automatic Classification Approach : 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


Literature Review : Literature Review


Stakeholder Analysis : 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, …


Slide9 : These types, ordered by their relevance to those appearing on the Web, are important for practical understanding of stakeholders of firms


Slide10 : 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) *


Comments on Stakeholder Research : 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


BI and Stakeholder Analysis : 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


Information on the Web : 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


Web Page Classification : 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


Feature selection in Web Page Classification : 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 out-links, 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


Research Questions : Research Questions


Research Gaps : 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


Research Questions : 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?


Research Approach and Testbed : Research Approach and Testbed


Automatic Classification Approach : 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


Building a Research Testbed : 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)


Creation of a Domain Lexicon : 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.,


Automatic Stakeholder Classification : Automatic Stakeholder Classification Three steps: Manual Tagging Feature selection Automatic classification


Manual Tagging : 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) Manual tagging Feature selection Automatic classification


Feature Selection : 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 Manual tagging Feature selection Automatic classification


An Example(a media type) : ...

I just saw a demo by ClearForest, 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. ...

... An Example (a media type) Link to the host company (ClearForest) HTML hyperlink and extended anchor text


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 Manual tagging Automatic classification Feature selection


Evaluation Methodology : Evaluation Methodology


Experimental Design : 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


Performance Measures : Performance Measures Effectiveness: Overall accuracy Within-class accuracy Efficiency: time used (in minutes) User subjective ratings and comments


User Study : 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


Hypotheses (1) : 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


Hypotheses (2) : 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


Hypotheses (3) : 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


Experimental Results and Discussion : Experimental Results and Discussion


Algorithm Comparison : 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


Effectiveness of the Approach : 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


Comparing with Human Judgment : 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


However, the algorithms achieved better within-class accuracies than humans in frequently occurring types … : However, the algorithms achieved better within-class accuracies than humans in frequently occurring types …


Use of Features : 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


Subjects’ Comments : Subjects’ Comments Overwhelmingly positive “It would be very helpful!” “That’s cool!” “I want to use it.”


Conclusions and Future Directions : Conclusions and Future Directions


Conclusions : Conclusions Proposed an automatic classification approach to business stakeholder analysis on the Web Integrated Human expert knowledge + machine-learned 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


Future Directions : 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