ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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Textmining anwendbare, Methodik überführt. Der Erfolg dieses Ansatzes wird durch Vergleich mit der „frequency baseline“ belegt. Die sechste Studie befasst sich mit der automatischen Identifikation von Ideen, die Konsumentenbedürfnisse darstellen. Anregungen, Wünsche und Vorstellungen, die Konsumenten über Produkte in Internet Blogs veröffentlichen, werden hinsichtlich neuer Ideen analysiert. Hierzu wird die Methodik aus der vierten Studie modifiziert sowie durch Elemente aus dem Web Content Mining erweitert. Es wird nachgewiesen, dass trotz Verwendung der wenig genormten Umgangssprache, eine automatische Identifikation von Konsumentenideen erfolgreich ist. Eine wichtige Aufgabe im Rahmen der Entwicklung neuer Produkte ist die Erstellung einer technologischen „Marktübersicht“, bei der technologische Einflüsse und Trends der eigenen Organisation denen der Konkurrenten gegenüber gestellt werden. Einen neuen Ansatz, um dies bei größeren Organisationen z.B. Regierungsorganisationen umzusetzen, ist in Studie 7 präsentiert. Die Methodik hinter diesem Ansatz verbindet die Cross-Impact Methode, um technologische Beziehungen aufzuzeigen, mit Methoden des Textminings, um Beschreibungen organisationseigener F&E-Projekte mit Patente der Konkurrenten vergleichen und zu Technologien zuordnen zu können. Im Ergebnis können die relativen technologischen Stärken und Schwächen der Organisation aufgezeigt werden und Mitarbeiter des Marketings bei der Erstellung der technologischen „Marktübersicht“ unterstützt werden. _______________________________________________________________________ 10
4. Summary Data mining is defined as the process of extracting patterns from data. Thus, it is used to transform this data into information [54]. A specific area of data mining is text mining or text data mining as the process of deriving high quality information from texts (unstructured data) [61]. Text mining structures the input text in a first step, it identifies new and unseen patterns within the structured textual data in a second step, and in a third step, it evaluates and interprets the results. Closely related to text mining is web content mining [56] as process of deriving high quality information from the content of web pages. In total, this dissertation shows how methods from text mining and from web content mining can be used to support marketing professionals by improving marketing decision-making. The methodologies are presented in several studies where relevant textual information is analyzed that can be found in different sources (e.g. in web pages, documents, research papers, articles in technical periodicals, reports, etc.) [53]. This dissertation consists of seven studies. In two studies, this dissertation supports marketing professionals by identifying profitable customers and companies. This dissertation contains further five studies, which contribute to methods from text mining into the marketing - R&D interface of the new product development process. Table 1 gives an overview of the different studies. Title Published Study 1 Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing Study 2 Predicting E-Commerce Company Success by Mining the Text of Its Publicly-Accessible Website Study 3 Finding New Technological Ideas and Inventions with Text Mining and Technique Expert Systems with Applications (in review) The Journal of Applied Research and Technology (in review) Schmidt-Thieme, L. (editor): Data Analysis, Machine Learning, and Applications, Proceedings of the 31st Annual Conference of _______________________________________________________________________ 11
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
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Page 19 and 20: Study 3 shows that a semi-automatic
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4.3 Comparing predictive performanc
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4.4 Results Predicting E-Commerce S
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Predicting E-Commerce Success using
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Predicting E-Commerce Success using
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Chapter III Finding new technologic
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Finding New Technological Ideas Fin
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Finding New Technological Ideas to
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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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