ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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Figure 1: Methodology of the approach 3.1 Data collection Analyzing existing Customers’ Websites For the data collection phase, structured customer information is needed to collect unstructured content information from customers’ websites. Structured customer information can be extracted from CRM systems of a company in which sales volume as well as e-mail addresses or website addresses for each customer is stored. Fig. 2 shows different steps in the data collection phase. _______________________________________________________________________ 32
Fig. 2: Different steps in data collection phase. Analyzing existing Customers’ Websites Information about existing customers’ sales volume is used to classify companies as profitable. An aggregation of the sales volume that belongs to the same company is done because several customers probably belong to the same company. Then, companies are assigned to a sales volume and they are defined as profitable if their volume exceeds a specific threshold. To identify customers’ company websites, the website addresses are used or if unavailable, e-mail addresses are converted to website addresses. In general, an e-mail address from a business customer based on the corresponding company website e.g. miller@companyname.com. Therefore, it is often possible to identify the corresponding company website for each customer. If a customer’s e-mail address is based on an ISP (internet-service provider) or e-mail provider (e.g. hotmail.com) then his company’s website is identified manually, otherwise information about this customer is discarded for further processing. A website consists of several web pages. To extract information from customers’ websites, relevant web pages have to be identified first. This avoids crawling trivial web pages e.g. ‘disclaimer’, ‘privacy / data protection policy’, ‘sitemap’, ‘about’, ‘contact formulary’ etc. In general, the starting page of an internet website is relevant. To identify further relevant web pages, the corpus of an internet search engine is used by access to web services. A web _______________________________________________________________________ 33
Page 1 and 2: ESSAYS ON TEXT MINING FOR IMPROVED
Page 3 and 4: Table of Contents 1. Acknowledgment
Page 5 and 6: 2. Nederlandstalige Samenvatting In
Page 7 and 8: ideeën zijn. Ten einde dit doel te
Page 9 and 10: Ein wesentlicher Erfolgsfaktor für
Page 11 and 12: 4. Summary Data mining is defined a
Page 13 and 14: from customers' and companies' webs
Page 15 and 16: of e-commerce companies divided int
Page 17 and 18: that provides commentary, news, and
Page 19 and 20: Study 3 shows that a semi-automatic
Page 21 and 22: take the granularity of the context
Page 23 and 24: [25] Herring, S.C., Scheidt, L.A.,
Page 25 and 26: [56] Thorleuchter, D., Van den Poel
Page 27 and 28: Table of Contents Analyzing existin
Page 29 and 30: 1 Introduction Analyzing existing C
Page 31: Analyzing existing Customers’ Web
Page 35 and 36: Analyzing existing Customers’ Web
Page 39 and 40: 3.4 Website clustering Analyzing ex
Page 41 and 42: 4 Empirical verification 4.1 Resear
Page 47 and 48: 5 Conclusion Analyzing existing Cus
Page 51 and 52: Chapter II Predicting E-Commerce Co
Page 53 and 54: Predicting E-Commerce Success using
Page 59 and 60: with , )} x T = the training set, }
Page 61 and 62: 4.3 Comparing predictive performanc
Page 63 and 64: 4.4 Results Predicting E-Commerce S
Page 69 and 70: Chapter III Finding new technologic
Page 71 and 72: Finding New Technological Ideas Fin
Page 73 and 74: Finding New Technological Ideas to
Page 75 and 76: Finding New Technological Ideas 3.
Page 77 and 78: p( Ξ raw δ ,.., δ ) 1 μ ⎧ raw
Page 79 and 80: Finding New Technological Ideas inv
Page 81 and 82: Chapter IV Mining Ideas from Textua
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Mining Ideas from Textual Informati
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1.2. Idea Definition Mining Ideas f
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3. Idea Mining Process Mining Ideas
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6. Results and Discussions Mining I
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Chapter V Mining Innovative Ideas t
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Mining Innovative Ideas to Support
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Mining Innovative Ideas idea is a n
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Mining Innovative Ideas identifies
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Mining Innovative Ideas categories
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9. Outlook Mining Innovative Ideas
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Chapter VI Extracting Consumers Nee
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Extracting Consumers Needs for New
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2. Rationale Behind this Approach E
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Bibliography Extracting Consumers N
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Table of Contents A Compared Cross
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A Compared Cross Impact Analysis Ke
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A Compared Cross Impact Analysis Th
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A Compared Cross Impact Analysis te
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A Compared Cross Impact Analysis In
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A Compared Cross Impact Analysis De
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis Ta
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A Compared Cross Impact Analysis Ea
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Table 3: Technology area pairs with
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A Compared Cross Impact Analysis A
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A Compared Cross Impact Analysis al
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A Compared Cross Impact Analysis Th
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A Compared Cross Impact Analysis [2
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