Decision Support Systems Text and Web Mining Learning

Decision Support Systems Text and Web Mining

Learning Objectives n n n 1 -2 n 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 Describe Web mining, its objectives, and its Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Concepts n n 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 n n A semi-automated process of extracting knowledge from unstructured data sources a. k. a. text data mining or knowledge discovery in textual databases 1 -3 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Data Mining versus Text Mining n n n Both seek for novel and useful patterns Both are semi-automated processes Difference is the nature of the data: n n n 1 -4 n 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 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Concepts n Benefits of text mining are obvious especially in text-rich data environments n n Electronic communication records (e. g. , Email) n n 1 -5 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. n Spam filtering Email prioritization and categorization Automatic response generation Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Application Area Information extraction n Topic tracking n Summarization n Categorization n Clustering n Concept linking n Question answering n 1 -6 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Terminology Unstructured or semistructured data n Corpus (and corpora) n Terms n Concepts n Stemming n Stop words (and include words) n Synonyms (and polysemes) n Tokenizing n 1 -7 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Terminology Term dictionary n Word frequency n Part-of-speech tagging n Morphology n Term-by-document matrix n n n 1 -8 Occurrence matrix Singular value decomposition n Latent semantic indexing Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining for Patent Analysis n What is a patent? n “exclusive rights granted by a country to an inventor for a limited period of time in exchange for a disclosure of an invention” How do we do patent analysis (PA)? n Why do we need to do PA? n 1 -9 n What are the benefits? Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Natural Language Processing (NLP) n Structuring a collection of text n n n NLP is … n n n 1 -10 n Old approach: bag-of-words New approach: natural language processing a very important concept in text mining a subfield of artificial intelligence and computational linguistics the studies of "understanding" the natural human language Syntax versus semantics based text Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Natural Language Processing (NLP) n What is “Understanding” ? Human understands, what about computers? n Natural language is vague, context driven n True understanding requires extensive knowledge of a topic n 1 -11 n Can/will computers ever understand Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Natural Language Processing (NLP) n Challenges in NLP n n n n Dream of AI community n 1 -12 Part-of-speech tagging Text segmentation Word sense disambiguation Syntax ambiguity Imperfect or irregular input Speech acts to have algorithms that are capable of automatically reading and obtaining knowledge Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Natural Language Processing (NLP) n Word. Net n n Sentiment Analysis n 1 -13 A laboriously hand-coded database of English words, their definitions, sets of synonyms, and various semantic relations between synonym sets A major resource for NLP Need automation to be completed A technique used to detect favorable and unfavorable opinions toward specific products and services Modified from Decision Support Systems and Business Intelligence Systems 9 E.

NLP Task Categories n n n Information retrieval Information extraction Named-entity recognition Question answering Automatic summarization Natural language generation and understanding Machine translation Foreign language reading and writing Speech recognition Text proofing Optical character recognition 1 -14 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Applications 1 -15 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Applications 1 -16 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process Context diagram for the text mining process 1 -17 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process The three-step text mining process 1 -18 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process n Step 1: Establish the corpus Collect all relevant unstructured data (e. g. , textual documents, XML files, emails, Web pages, short notes, voice recordings…) n Digitize, standardize the collection Place the collection in a common place (e. g. , in a flat file, or in a n 1 -19 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process n Step 2: Create the Term–by–Document Matrix 1 -20 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process n Step 2: Create the Term–by– Document Matrix (TDM) n Should all terms be included? Stop words, include words n Synonyms, homonyms n Stemming n n 1 -21 What is the best representation of the indices n Row counts; binary frequencies; log frequencies; n Inverse document frequency Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process n Step 2: Create the Term–by–Document Matrix (TDM) n TDM is a sparse matrix. How can we reduce the dimensionality of the TDM? Manual - a domain expert goes through it n Eliminate terms with very few occurrences in very few documents n Transform the matrix usingular value decomposition (SVD) n SVD is similar to principle component analysis n 1 -22 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Text Mining Process n Step 3: Extract patterns/knowledge Classification (text categorization) n Clustering (natural groupings of text) n Improve search recall n Improve search precision n Scatter/gather n Query-specific clustering n 1 -23 Association n. Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Mining Overview n n n Web is the largest repository of data Data is in HTML, XML, text format Challenges (of processing Web data) n n n The Web is too big for effective data mining The Web is too complex The Web is too dynamic The Web is not specific to a domain The Web has everything 1 -24 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Mining n Web mining (or Web data mining) is the process of discovering intrinsic relationships from Web data (textual, linkage, or usage) 1 -25 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Content/Structure Mining of the textual content on the Web n Data collection via Web crawlers n Web pages include hyperlinks n Authoritative pages n Hubs n hyperlink-induced topic search (HITS) n 1 -26 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Usage Mining n Extraction of information from data generated through Web page visits and transactions… n n 1 -27 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 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Usage Mining n Web usage mining applications Determine the lifetime value of clients n Design cross-marketing strategies across products. n Evaluate promotional campaigns n Target electronic ads and coupons at user groups based on user access patterns n Predict user behavior based on previously learned rules and users' profiles Modified from Decision Support Systems and Business Intelligence Systems 9 E. n 1 -28

Web Usage Mining (clickstream analysis) 1 -29 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Mining Success Stories n n Amazon. com, Ask. com, Scholastic. com, … Website Optimization Ecosystem 1 -30 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

Web Mining Tools 1 -31 Modified from Decision Support Systems and Business Intelligence Systems 9 E.

End of the Chapter n Questions / comments… 1 -32 Modified from Decision Support Systems and Business Intelligence Systems 9 E.