Text Mining and Its Applications to Intelligence, CRM and Knowledge Management
Published on 30. September 2007
Book
Paperback/Softback
388 pages
978-1-84564-131-3 (ISBN)
Description
Following the success of the first edition of this book published two years ago, this New Edition, now in paperback format, has been updated and includes new data on the main market players (28 companies are described) to reflect the latest changes and developments within the text mining sector. Text Mining is an interdisciplinary field bringing together techniques from data mining, linguistics, information retrieval, and visualization to address the issue of quickly extracting information from large databases with different applicative objectives.This book is directed towards graduate students in business, and undergraduate students in computer science, and to practitioners in law enforcement, security, intelligence, marketing and IT departments; it assumes readers have little or no previous knowledge about mathematics or linguistics. It has been structured as a self-teaching guide and has been written as a result of the authors' experiences in participating in several large-scale text mining projects.It can be used as a guide for system integrators, and designers of text mining systems, but especially for business analysts and consultants who wish to apply the powerful tools of this technology to real situations.
More details
Series
Language
English
Place of publication
Southampton
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 230 mm
Width: 155 mm
ISBN-13
978-1-84564-131-3 (9781845641313)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Alessandro Zanasi is a security research advisor and professor at Bologna University, Italy. Before he served as Carabinieri officer in Rome Scientific Investigations Center; IBM executive in Italy, Paris and San Jose (USA); META Group analyst; cofounder of Temis SA.As an intelligence specialist, he has been advising governments and corporations in security, intelligence and detection technologies for more than twenty years. Among the others: European Commission through his membership, since 2005, to ESRAB-European Security Research Advisory Board and, since 2007, to ESRIF-European Security Research and Innovation Forum.
Content
Part 1: THEORETICAL OVERVIEWChapter 1: Text processing and information retrievalIntroduction; Data gathering and extraction of text; Text processing; Information retrieval; Concluding remarksChapter 2: Information extraction and ...SurroundingsIntroduction; Information extraction historical flash-back; IE systems architecture; Features of an IE system; Adaptive IE systems; IE systems: a few European applicationsChapter 3: Text clustering as a mining taskIntroduction; Overview on data clustering analysis; Problems and solutions in the text clustering field; ConclusionsChapter 4: Text categorizationIntroduction; The basic picture; Techniques; Applications; Conclusion; NotesChapter 5: Summarization and visualizationIntroduction; Text summarization; Text visualization; Example of summarization of a document set; Future directions of research and applicationsPart 2: APPLICATIONSChapter 6: Application integration in applied text miningIntroduction; Business drivers and application types; Application elements; ConclusionsChapter 7: ROI in text mining projectsIntroduction; The evaluation of a text mining solution; The evaluation of the tangible components; The evaluation of the intangible components; Conclusionsa) INTELLIGENCEChapter 8: Open sources automatic analysis for corporate and government intelligenceIntroduction; New government intelligence role; Corporate intelligence; Open sources; Terrorism and other challenges to government intelligence; Practical examples of text mining applied to the intelligence process; Business casesChapter 9: A critical appraisal of text mining in an intelligence environmentIntroduction; 11 Sept., intelligence and information explosion; Data mining: some world relevant examples; Data mining, the intelligence cycle and decisionChapter 10: Marketing intelligence system to forecast telecommunications competitive landscapeIntroduction; Italian mobile market overview; TIM positioning; From competitive to market intelligence; Our needsChapter 11: Competitive intelligence for SMEs: An application to the Italian building sectorWhat was the problem; Edilintelligence: what is it?; The text mining bricks of the solution: Theory and practice; Conclusionsb) CRMChapter 12: Virtual communities: human capital and other personal characteristics extractionThe emergence of neo-renaissance paradigm; Intellectual and human capital;Virtual communities: where text mining is applied; Human capital in customer communities; Human capital in employee community; Human capital in social contexts; Social network links detectionChapter 13: Customer feedbacks and opinion surveys analysis in the automotive industryIntroduction; Customer feedback analysis in Renault; Opinion surveys for automotive manufacturers; ConclusionsChapter 14: The Responsio email management systemIntroduction; Email answering by semi-supervised text classification; Responsio email management system; Case study; DiscussionChapter 15: TV channel provider: mining the user feedbackIntroduction; The case; The process; Conclusionc) KNOWLEDGE MANAGEMENTChapter 16: Text mining based knowledge management in bankingIntroduction; The document as a primary source; Knowledge based search; Building up a knowledge management infrastructure; Integrating principles; Modules; Conclusion and future workChapter 17: Text mining in life sciencesIntroduction; Text mining - current state; Ontology development; ConclusionChapter 18: Information search and classification to foster innovation in SMEs The AREA Science Park experienceThe AREA Science Park and its technology transfer division; TEMIS online miner light, the TTD search engine for patents (TTDSE); TTD resultsChapter 19: Media industry: how to improve documentalists efficiencyIntroduction; Text data production in media; Indexing textual data; Archive solutions: data bases and automatic procedures; Text mining experience in Gruner + Jahr; ConclusionChapter 20: Link analysis in crime pattern detectionIntroduction; Case overview; Implementation approach; Data preprocessing; Structured data analysis; Concept extraction; Pattern analysis; Drill-down and reporting; Drill-down and reporting; Automation; ConclusionPart 3: SOFTWARE and SERVICESChapter 21: Text mining resourcesIntroductionA: SOFTWAREB: SERVICES