1. Authors:Jochen Dijrre, Peter Gerstl, Roland Seiffert Adapted from slides by: Trevor Crum Presenter: Nicholas Romano Text Mining: Finding Nuggets in Mountains of Textual Data 1

2. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 2

3. Definition ●Text Mining: ○ The discovery by computer of new, previously unknown information, by automatically extracting information from different unstructured textual documents. ○ Also referred to as text data mining, roughly equivalent to text analytics which refers more specifically to problems based in a business settings. 3

4. Paper Overview ●This paper introduced text mining and how it differs from data mining proper. ●Focused on the tasks of feature extraction and clustering/categorization ●Presented an overview of the tools/methods of IBM’s Intelligent Miner for Text 4

5. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 5

6. Motivation ●A large portion of a company’s data is unstructured or semi-structured – about 90% in 1999! Letters Emails Phone transcripts Contracts Technical documents Patents Web pages Articles 6

7. Typical Applications ●Summarizing documents ●Discovering/monitoring relations among people, places, organizations, etc ●Customer profile analysis ●Trend analysis ●Document summarization ●Spam Identification ●Public health early warning ●Event tracks 7

8. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Comparison with Data Mining ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Conclusion & Exam Questions 8

9. Methodology: Challenges ●Information is in unstructured textual form ●Natural language interpretation is difficult & complex task! (not fully possible) ○ Google and Watson are a step closer ●Text mining deals with huge collections of documents ○ Impossible for human examination 9

10. Google vs Watson ●Google justifies the answer by returning the text documents where it found the evidence. ●Google finds documents that are most suitable to a given Keyword. ●Watson tries to understand the semantics behind a given key phrase or question. ●Then Watson will use its huge knowledge base to find the correct answer. 10

11. Methodology: Two Aspects ●Knowledge Discovery ○ Extraction of codified information ■ Feature Extraction ○ Mining proper; determining some structure ●Information Distillation ○ Analysis of feature distribution 11

12. Two Text Mining Approaches ●Extraction ○ Extraction of codified information from a single document ●Analysis ○ Analysis of the features to detect patterns, trends, and other similarities over whole collections of documents 12

13. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 13

14. Feature Extraction ●Recognize and classify “significant” vocabulary items from the text ●Categories of vocabulary ○ Proper names – Mrs. Albright or Dheli, India ○ Multiword terms – Joint venture, online document ○ Abbreviations – CPU, CEO ○ Relations – Jack Smith-age-42 ○ Other useful things: numerical forms of numbers, percentages, money, dates, and many other 14

15. Canonical Form Examples ●Normalize numbers, money ○ Four = 4, five-hundred dollars = $500 ●Conversion of date to normal form ○ 8/17/1992 = August 18 1992 ●Morphological variants ○ Drive, drove, driven = drive ●Proper names and other forms ○ Mr. Johnson, Bob Johnson, The author = Bob Johnson 15

16. Feature Extraction Approach ●Linguistically motivated heuristics ●Pattern matching ●Limited lexical information (part-of-speech) ●Avoid analyzing with too much depth ○ Does not use too much lexical information ○ No in-depth syntactic or semantic analysis 16

17. IBM Intelligent Miner for Text ●IBM introduced Intelligent Miner for Text in 1998 ●SDK with: Feature extraction, clustering, categorization, and more ●Traditional components (search engine, etc) 17

18. Advantages to IBM’s approach ●Processing is very fast (helps when dealing with huge amounts of data) ●Heuristics work reasonably well ●Generally applicable to any domain 18

19. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Comparison with Data Mining ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Conclusion & Exam Questions 19

20. Clustering ●Fully automatic process ●Documents are grouped according to similarity of their feature vectors ●Each cluster is labeled by a listing of the common terms/keywords ●Good for getting an overview of a document collection 20

21. Two Clustering Engines ●Hierarchical clustering ○ Orders the clusters into a tree reflecting various levels of similarity ●Binary relational clustering ○ Flat clustering ○ Relationships of different strengths between clusters, reflecting similarity 21

22. Clustering Model 22

23. Categorization ●Assigns documents to preexisting categories ●Classes of documents are defined by providing a set of sample documents. ●Training phase produces “categorization schema” ●Documents can be assigned to more than one category ●If confidence is low, document is set aside for human intervention 23

24. Categorization Model 24

25. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 25

26. Applications ●Customer Relationship Management application provided by IBM Intelligent Miner for Text called “Customer Relationship Intelligence” or CRI ○ “Help companies better understand what their customers want and what they think about the company itself” 26

27. Customer Intelligence Process ●Take as input body of communications with customer ●Cluster the documents to identify issues ●Characterize the clusters to identify the conditions for problems ●Assign new messages to appropriate clusters 27

28. Customer Intelligence Usage ●Knowledge Discovery ○ Clustering used to create a structure that can be interpreted ●Information Distillation ○ Refinement and extension of clustering results ■ Interpreting the results ■ Tuning of the clustering process ■ Selecting meaningful clusters 28

29. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 29

30. Comparison with Data Mining ●Data mining ○ Discover hidden models. ○ tries to generalize all of the data into a single model. ○ marketing, medicine, health care ●Text mining ○Discover hidden facts. ○tries to understand the details, cross reference between individual instances ○ biosciences, customer profile analysis 30

31. Outline ●Definition and Paper Overview ●Motivation ●Methodology ●Feature Extraction ●Clustering and Categorizing ●Some Applications ●Comparison with Data Mining ●Conclusion & Exam Questions 31

32. Conclusion ●Text mining can be used as an effective business tool that supports ○ Creation of knowledge by preparing and organizing unstructured textual data [Knowledge Discovery] ○ Extraction of relevant information from large amounts of unstructured textual data through automatic pre- selection based on user defined criteria [Information Distillation] 32

33. Exam Question #1 ●How does the procedure for text mining differ from the procedure for data mining? ○ Adds feature extraction phase ○ Infeasible for humans to select features manually ○ The feature vectors are, in general, highly dimensional and sparse 33

34. Questions? 34

35. Web Mining Research: A Survey Authors: Raymond Kosala & Hendrik Blockeel Presenter: Nick Romano Slides adapted from: Ryan Patterson April 23rd 2014 CS332 Data Mining pg 01

36. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 03

37. Introduction “The Web is huge, diverse, and dynamic... we are currently drowning in information and facing information overload.” Web users encounter problems: Finding relevant information Creating new knowledge out of the information available on the Web Personalization of the information Learning about consumers or individual users pg 04

38. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 05

39. Web Mining “Web mining is the use of data mining techniques to automatically discover and extract information from Web documents and services.” Web mining subtasks: 1.Resource finding 2.Information selection and pre-processing 3.Generalization 4.Analysis pg 06

40. Information Retrieval & Information Extraction Information Retrieval (IR) o the automatic retrieval of all relevant documents while at the same time retrieving as few of the non- relevant as possible Information Extraction (IE) o transforming a collection of documents into information that is more readily digested and analyzed pg 07

41. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 09

42. Web Content Mining Information Retrieval View Unstructured Documents Most utilizes “bag of words” representation to generate documents features oignores the sequence in which the words occur Document features can be reduced with selection algorithms oie. information gain Possible alternative document feature representations: oword positions in the document ophrases/terms (ie. “annual interest rate”) Semi-Structured Documents Utilize additional structural information gleaned from the document oHTML markup (intra-document structure) oHTML links (inter-document structure) pg 10

43. Web content mining, IR unstructured documents pg 11

44. Web content mining, IR semi structured documents pg 12

45. Web Content Mining Database View “the Database view tries... to transform a Web site to become a database so that... querying on the Web become[s] possible.” Uses Object Exchange Model (OEM) orepresents semi-structured data by a labeled graph Database view algorithms typically start from manually selected Web sites osite-specific parsers Database view algorithms produce: oextract document level schema or DataGuides ▪structural summary of semi-structured data oextract frequent substructures (sub-schema) omulti-layered database ▪each layer is obtained by generalizations on lower layers pg 13

46. Web content mining, Database view pg 14

47. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 15

48. Web Structure Mining “... we are interested in the structure of the hyperlinks within the Web itself” Inspired by the study of social networks and citation analysis obased on incoming & outgoing links we could discover specific types of pages (such as hubs, authorities, etc) Some algorithms calculate the quality/relevancy of each Web page oie. Page Rank Others measure the completeness of a Web site omeasuring frequency of local links on the same server ointerpreting the nature of hierarchy of hyperlinks on one domain pg 16

49. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 17

50. Web Usage Mining “... focuses on techniques that could predict user behavior while the user interacts with the Web.” Web usage is mined by parsing Web server logs omapped into relational tables → data mining techniques applied olog data utilized directly Users connecting through proxy servers and/or users or ISP’s utilizing caching of Web data results in decreased server log accuracy Two applications: opersonalized - user profile or user modeling in adaptive interfaces oimpersonalized - learning user navigation patterns pg 18

51. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 19

52. Review Web mining o 4 subtasks o IR & IE Web content mining o primarily intra-page analysis o IR view vs DB view Web structure mining o primarily inter-page analysis Web usage mining o primarily analysis of server activity logs pg 20

53. Web mining categories Web Mining Web Content Mining Web Structure MiningWeb Usage Mining IR ViewDB View View of Data - Unstructured - Semi structured - Web site as DB - Links structure- Interactivity Main Data - Text documents - Hypertext documents - Links structure- Server logs - Browser logs Representation - Bag of word, n-grams - Terms, phrases - Concepts of ontology - Relational - Edge-labeled graph (OEM) - Relational - Graph- Relational table - Graphs Method - TFIDF and variants - Machine learning - Statistical (incl. NLP) - Proprietary algorithms - ILP - (modified) association rules - Proprietary algorithms- Machine Learning - Statistical - (modified) association rules Application Categories - Categorization - Clustering - Finding extraction rules - Finding patterns in text - User modeling - Finding frequent sub- structures - Web site schema discovery - Categorization - Clustering - Site construction, adaptation, and management - Marketing - User modeling pg 21

54. outline Introduction Web Mining Web Content Mining Web Structure Mining Web Usage Mining Review Exam Questions pg 22

55. Exam Question 2 Q:Of the following Web mining paradigms: Information Retrieval Information Extraction Which does a traditional Web search engine (google.com, bing.com, etc.) attempt to accomplish? Briefly support your answer. A:Information Retrieval, the search engine attempts provides a list of documents ranked by their relevancy to the search query. pg 24

56. Exam Question 3 Q:State one common problem hampering accurate Web usage mining? Briefly support your answer. A: Users connecting to a Web site though a proxy server, Users (or their ISP’s) utilizing Web data caching,will result in decreased server log accuracy. Accurate server logs are required for accurate Web usage mining. pg 26

57. Exam Question 1 (Again) ●How does the procedure for text mining differ from the procedure for data mining? ○ Adds feature extraction phase ○ Infeasible for humans to select features manually ○ The feature vectors are, in general, highly dimensional and sparse 57

58. Questions? 58