ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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Chapter I Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing This chapter is based on Thorleuchter, D., Van den Poel, D., Prinzie, A. Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing. Expert Systems with Applications (in review).
Table of Contents Analyzing existing Customers’ Websites 1 Introduction .....................................................................................................................29 2 Related Work ..................................................................................................................30 3 Methodology....................................................................................................................31 3.1 Data collection ........................................................................................................32 3.2 Pre-processing .......................................................................................................34 3.2.1 Text preparation..................................................................................................35 3.2.2 Term filtering.......................................................................................................35 3.2.3 Term vector weighting ........................................................................................36 3.2.4 Term vector aggregation.....................................................................................36 3.3 Concept identification with LSI and singular value decomposition .........................37 3.3.1 Feature space dimension reduction....................................................................37 3.3.2 Concept dimension selection..............................................................................38 3.3.3 Projection of test examples into the LSI-subspace.............................................38 3.4 Website clustering ..................................................................................................39 3.5 Prediction Modeling ................................................................................................39 3.6 Evaluation criteria ...................................................................................................40 4 Empirical verification .......................................................................................................41 4.1 Research data ........................................................................................................41 4.2 Optimal dimension selection...................................................................................41 4.3 Creating and comparing address lists ....................................................................43 4.4 Comparing predictive performance.........................................................................44 5 Conclusion ......................................................................................................................47 _______________________________________________________________________ 27
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: [56] Thorleuchter, D., Van den Poel
Page 29 and 30: 1 Introduction Analyzing existing C
Page 31 and 32: Analyzing existing Customers’ Web
Page 33 and 34: Fig. 2: Different steps in data col
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
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Page 75 and 76: Finding New Technological Ideas 3.
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p( Ξ raw δ ,.., δ ) 1 μ ⎧ raw
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Finding New Technological Ideas inv
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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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Table 3: Technology area pairs with
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