Decision Support and Business Intelligence Systems (9th Ed., Prentice Hall) Chapter 7: Text and Web Mining Learning Objectives      7-2 Describe text mining and understand the need for text mining Differentiate between text mining, Web mining and data mining Understand the different application areas for text mining Know the process of carrying out a text mining project Understand the different methods to introduce structure to text-based data Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Learning Objectives   Describe Web mining, its objectives, and its benefits Understand the three different branches of Web mining     7-3 Web content mining Web structure mining Web usage mining Understand the applications of these three mining paradigms Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Opening Vignette: “Mining Text for Security and Counterterrorism”  What is MITRE?  Problem description  Proposed solution  Results  Answer and discuss the case questions 7-4 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Opening Vignette: Mining Text For Security… 7-5 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Concepts     85-90 percent of all corporate data is in some kind of unstructured form (e.g., text) Unstructured corporate data is doubling in size every 18 months Tapping into these information sources is not an option, but a need to stay competitive Answer: text mining   7-6 A semi-automated process of extracting knowledge from unstructured data sources a.k.a text data mining or knowledge discovery in textual databases Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Data Mining versus Text Mining     7-7 Both seek for novel and useful patterns Both are semi-automated processes Difference is the nature of the data:  Structured versus unstructured data  Structured data: in databases  Unstructured data: Word documents, PDF files, text excerpts, XML files, and so on Text mining – first, impose structure to the data, then mine the structured data Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Concepts  Benefits of text mining are obvious especially in text-rich data environments   Electronic communization records (e.g., Email)    7-8 e.g., law (court orders), academic research (research articles), finance (quarterly reports), medicine (discharge summaries), biology (molecular interactions), technology (patent files), marketing (customer comments), etc Spam filtering Email prioritization and categorization Automatic response generation Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Application Area        7-9 Information extraction Topic tracking Summarization Categorization Clustering Concept linking Question answering Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Terminology         7-10 Unstructured or semistructured data Corpus (and corpora) Terms Concepts Stemming Stop words (and include words) Synonyms (and polysemes) Tokenizing Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Application (research trend identification in literature)  Mining the published IS literature        7-31 MIS Quarterly (MISQ) Journal of MIS (JMIS) Information Systems Research (ISR) Covers 12-year period (1994-2005) 901 papers are included in the study Only the paper abstracts are used clusters are generated for further analysis Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Application (research trend identification in literature) 7-32 Journal Year Author(s) MISQ 2005 A Malhotra, S Gosain and O A El Sawy ISR 1999 JMIS 2001 R Aron and E K Clemons … … … Title Vol/No Pages Absorptive capacity configurations in supply chains: Gearing for partnerenabled market knowledge creation D Robey and Accounting for the M C Boudreau contradictory organizational consequences of information technology: Theoretical directions and methodological implications Keywords Abstract 145-187 knowledge management supply chain absorptive capacity interorganizational information systems configuration approaches 2-Oct 167-185 organizational transformation impacts of technology organization theory research methodology intraorganizational power electronic communication mis implementation culture systems Achieving the optimal 18/2 65-88 information products balance between internet advertising investment in quality product positioning and investment in selfsignaling promotion for signaling games information products … 29/1 … … … Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall The need for continual value innovation is driving supply chains to evolve from a pure transactional focus to leveraging interorganizational partner ships for sharing Although much contemporary thought considers advanced information technologies as either determinants or enablers of radical organizational change, empirical studies have revealed inconsistent findings to support the deterministic logic implicit in such arguments This paper reviews the contradictory When producers of goods (or services) are confronted by a situation in which their offerings no longer perfectly match consumer preferences, they must determine the extent to which the advertised features of … No of Articles (research trend identification in literature) 7-33 3 2 1 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 C LU S TER : C LU STER : C LU STER : 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 5 5 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 5 5 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 3 2 1 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 5 5 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 3 2 1 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Text Mining Application C LU S TER : C LU STER : C LU STER : C LU S TER : C LU STER : C LU STER : Y EAR Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Text Mining Application (research trend identification in literature) 100 90 80 70 60 50 40 30 20 10 IS R J M IS M IS Q IS R No of Articles C LU S T ER : J M IS M IS Q IS R C LU S T ER : J M IS M IS Q C LU S T E R : 100 90 80 70 60 50 40 30 20 10 IS R J M IS M IS Q IS R C LU S T ER : J M IS M IS Q IS R C LU S T ER : J M IS M IS Q C LU S T E R : 100 90 80 70 60 50 40 30 20 10 IS R J M IS M IS Q C LU S T ER : IS R J M IS M IS Q C LU S T ER : JO U R N AL 7-34 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall IS R J M IS M IS Q C LU S T E R : Text Mining Tools   7-35 Commercial Software Tools  SPSS PASW Text Miner  SAS Enterprise Miner  Statistica Data Miner  ClearForest, … Free Software Tools  RapidMiner  GATE  Spy-EM, … Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Mining Overview    Web is the largest repository of data Data is in HTML, XML, text format Challenges (of processing Web data)       7-36 The The The The The Web Web Web Web Web is too big for effective data mining is too complex is too dynamic is not specific to a domain has everything Opportunities and challenges are great! Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Mining  7-37 Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Content/Structure Mining    7-38 Mining of the textual content on the Web Data collection via Web crawlers Web pages include hyperlinks  Authoritative pages  Hubs  hyperlink-induced topic search (HITS) alg Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Usage Mining  Extraction of information from data generated through Web page visits and transactions…      7-39 data stored in server access logs, referrer logs, agent logs, and client-side cookies user characteristics and usage profiles metadata, such as page attributes, content attributes, and usage data Clickstream data Clickstream analysis Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Usage Mining  Web usage mining applications       7-40 Determine the lifetime value of clients Design cross-marketing strategies across products Evaluate promotional campaigns Target electronic ads and coupons at user groups based on user access patterns Predict user behavior based on previously learned rules and users' profiles Present dynamic information to users based on their interests and profiles… Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Usage Mining (clickstream analysis) 7-41 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Mining Success Stories   7-42 Amazon.com, Ask.com, Scholastic.com, … Website Optimization Ecosystem Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Web Mining Tools 7-43 Product Name URL Angoss Knowledge WebMiner angoss.com ClickTracks clicktracks.com LiveStats from DeepMetrix deepmetrix.com Megaputer WebAnalyst megaputer.com MicroStrategy Web Traffic Analysis microstrategy.com SAS Web Analytics sas.com SPSS Web Mining for Clementine spss.com WebTrends webtrends.com XML Miner scientio.com Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall End of the Chapter  7-44 Questions / comments… Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Printed in the United States of America Copyright © 2011 Pearson Education, Inc   Publishing as Prentice Hall 7-45 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall ... Objectives      7-2 Describe text mining and understand the need for text mining Differentiate between text mining, Web mining and data mining Understand the different application areas for text... mining, its objectives, and its benefits Understand the three different branches of Web mining     7-3 Web content mining Web structure mining Web usage mining Understand the applications of...     7-10 Unstructured or semistructured data Corpus (and corpora) Terms Concepts Stemming Stop words (and include words) Synonyms (and polysemes) Tokenizing Copyright © 2011 Pearson Education,