ESSAYS ON TEXT MINING FOR IMPROVED DECISION MAKING ...

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As a result, this idea mining approach extracts automatically new and useful ideas from a user given text. The problem solution ideas are presented in a comprehensible way to the users [16, 21, 34]. This approach is evaluated with patent data and it is realized as webbased application, named "Technological Idea Miner" that can be used for further testing and evaluation. 5.6. Study 5: Mining Innovative Ideas to Support new Product Research and Development For new product development, a new idea with innovative potential as starting point is needed [41]. Unfortunately, a high percentage of innovations fail, which means many selected ideas do not have the potential to become an innovation in future [6]. Additionally, the process from a new idea as starting point via research, development, and production activities through to an innovative product is very cost- and time-consuming [12]. Thus, the main objective of study 5 is to identify the innovative potential of new technological ideas to improve the performance of the innovation process. The study 5 uses new technological ideas as inputs that are extracted from provided textual information by the application from study 4. It identifies innovative technology fields based on an approach from [46] by analyzing relationships among technologies [17]. All identified ideas are assigned to innovative technology fields by using text mining and text classification methods (tokenization [14], term filtering methods [27, 36], Jaccard's coefficient [15]). For this, the application from study 4 is extended with these innovation-related aspects. With this new application, technological ideas in innovative fields are presented to the user as innovative ideas and might be used as starting point for new product research and development divisions. 5.7. Study 6: Extracting Consumers Needs for New Products - A Web Mining Approach The purpose of study 6 is to introduce a web mining approach for automatically identifying new product ideas extracted from web logs. A web log - also known as blog - is a web site _______________________________________________________________________ 16
that provides commentary, news, and further information on a subject written by individual persons [64]. We can find a large amount of web logs for nearly each topic [25] where consumers present their needs for new products [51]. Finding these new product ideas is a well-known task in marketing [32, 35]. Therefore, with the automatically approach in study 6, we support marketing professionals by extracting new and useful product ideas from textual information in web logs. The approach in study 6 is based on the approach in study 4. However, we consider peculiarities like the usage of text-mining in the colloquial language field [26, 38]. This approach is implemented by a web-based application named "Product Idea Web Log Miner" where marketing professionals provide descriptions of existing products. As a result, new product ideas are extracted from the web logs and presented to the users. 5.8. Study 7: A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies The planning of technological research and development (R&D) is demanding in areas with many relationships between technologies [8]. To support decision makers of a government organization with R&D planning [39, 42] in these areas, a methodology to make the technology impact more transparent is introduced. The method shows current technology impact and impact trends from the R&D of an organization's competitors and compares these to the technology impact and impact trends from the organization's own R&D. This way, relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs can be identified. A quantitative cross impact analysis (CIA) approach [13, 29, 50] is used to estimate the impact across technologies. The quantitative CIA approach contrasts to standard qualitative CIA approaches [18] that estimate technology impact by means of literature surveys and expert interviews. In this study, the impact is computed based on the R&D information regarding the respective organization on one hand, and based on patent data [11, 59] representative regarding R&D information of the organization's competitors on the other hand. As an illustration, the application field 'defense' is used, where many interrelations and interdependencies between defense-based technologies occur [33, 55]. Firstly, an R&Dbased and patent-based Compared Cross Impact (CCI) among technologies is computed. Secondly, characteristics of the CCI are identified. Thirdly, the CCI data is presented as a _______________________________________________________________________ 17
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: of e-commerce companies divided int
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 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
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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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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 As
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A Compared Cross Impact Analysis Ta
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A Compared Cross Impact Analysis Th
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A Compared Cross Impact Analysis [2
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