LEADER 09139nam 2200565 450 001 9910806102003321 005 20230124200814.0 010 $a1-68392-664-1 010 $a1-68392-665-X 035 $a(CKB)4100000011971053 035 $a(MiAaPQ)EBC6647712 035 $a(Au-PeEL)EBL6647712 035 $a(OCoLC)1259323033 035 $a(DE-B1597)654019 035 $a(DE-B1597)9781683926658 035 $a(EXLCZ)994100000011971053 100 $a20220702d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aText analytics for business decisions $ea case study approach /$fAndres G. Fortino 210 1$aDulles :$cMercury Learning & Information,$d[2021] 210 4$dİ2021 215 $a1 online resource (332 pages) 327 $aIntro -- Contents -- Preface -- On the Companion Files -- Acknowledgements -- Chapter 1 : Framing Analytical Questions -- Data is the New Oil -- The World of the Business Data Analyst -- How Does Data Analysis Relate to Decision Making? -- How Do We Frame Analytical Questions? -- What are the Characteristics of Well-framed Analytical Questions? -- Exercise 1.1 - Case Study Using Dataset K: Titanic Disaster -- What are Some Examples of Text-Based Analytical Questions? -- Additional Case Study Using Dataset J: Remote Learning Student Survey -- References -- Chapter 2 : Analytical Tool Sets -- Tool Sets for Text Analytics -- Excel -- Microsoft Word -- Adobe Acrobat -- SAS JMP -- R and RStudio -- Voyant -- Java -- Stanford Named Entity Recognizer (NER) -- Topic Modeling Tool -- References -- Chapter 3 : Text Data Sources and Formats -- Sources and Formats of Text Data -- Social Media Data -- Customer opinion data from commercial sites -- Email -- Documents -- Surveys -- Websites -- Chapter 4 : Preparing the Data File -- What is Data Shaping? -- The Flat File Format -- Shaping the Text Variable in a Table -- Bag-of-Words Representation -- Single Text Files -- Exercise 4.1 - Case Study Using Dataset L: Resumes -- Exercise 4.2 - Case Study Using Dataset D: Occupation Descriptions -- Additional Exercise 4.3 - Case Study Using Dataset I: NAICS Codes -- Aggregating Across Rows and Columns -- Exercise 4.4 - Case Study Using Dataset D: Occupation Descriptions -- Additional Advanced Exercise 4.5 - Case Study Using Dataset E: Large Data Files -- Additional Advanced Exercise 4.6 - Case Study Using Dataset F: The Federalist Papers -- References -- Chapter 5 : Word Frequency Analysis -- What is Word Frequency Analysis? -- How Does It Apply to Text Business Data Analysis? -- Exercise 5.1 - Case Study Using Dataset A: Training Survey. 327 $aExercise 5.2 - Case Study Using Dataset D: Job Descriptions -- Exercise 5.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 5.4 - Case Study Using Dataset B: Consumer Complaints -- Chapter 6 : Keyword Analysis -- Exercise 6.1 - Case Study Using Dataset D: Resume and Job Description -- Exercise 6.2 - Case Study Using Dataset G: University Curriculum -- Exercise 6.3 - Case Study Using Dataset C: Product Reviews -- Additional Exercise 6.4 - Case Study Using Dataset B: Customer Complaints -- Chapter 7 : Sentiment Analysis -- What is Sentiment Analysis? -- Exercise 7.1 - Case Study Using Dataset C: Product Reviews - Rubbermaid -- Exercise 7.2 - Case Study Using Dataset C: Product Reviews-Windex -- Exercise 7.3 - Case Study Using Dataset C: Product Reviews-Both Brands -- Chapter 8 : Visualizing Text Data -- What Is Data Visualization Used For? -- Exercise 8.1 - Case Study Using Dataset A: Training Survey -- Exercise 8.2 - Case Study Using Dataset B: Consumer Complaints -- Exercise 8.3 - Case Study Using Dataset C: Product Reviews -- Exercise 8.4 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 9 : Coding Text Data -- What is a Code? -- What are the Common Approaches to Coding Text Data? -- What is Inductive Coding? -- Exercise 9.1 - Case Study Using Dataset A: Training -- Exercise 9.2 - Case Study Using Dataset J: Remote Learning -- Exercise 9.3 - Case Study Using Dataset E: Large Text Files -- Affinity Diagram Coding -- Exercise 9.4 - Case Study Using Dataset M: Onboarding Brainstorming -- References -- Chapter 10 : Named Entity Recognition -- Named Entity Recognition -- What is a Named Entity? -- Common Approaches to Extracting Named Entities -- Classifiers - The Core NER Process -- What Does This Mean for Business? -- Exercise 10.1 - Using the Stanford NER -- Exercise 10.2 - Example Cases. 327 $aExercise 10.2 - Case Study Using Dataset H: Corporate Financial Reports -- Additional Exercise 10.3 - Case Study Using Dataset L: Corporate Financial Reports -- Exercise 10.4 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 10.5 - Case Study Using Dataset E: Large Text Files -- References -- Chapter 11 : Topic Recognition in Documents -- Information Retrieval -- Document Characterization -- Topic Recognition -- Exercises -- Exercise 11.1 - Case Study Using Dataset G: University Curricula -- Exercise 11.2 - Case Study Using Dataset E: Large Text Files -- Exercise 11.3 - Case Study Using Dataset E: Large Text Files -- Exercise 11.4 - Case Study Using Dataset E: Large Text Files -- Exercise 11.5 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.6 - Case Study Using Dataset P: Patents -- Additional Exercise 11.7 - Case Study Using Dataset F: Federalist Papers -- Additional Exercise 11.8 - Case Study Using Dataset E: Large Text Files -- Additional Exercise 11.9- Case Study Using Dataset N: Sonnets -- References -- Chapter 12 : Text Similarity Scoring -- What is Text Similarity Scoring? -- Text Similarity Scoring Exercises -- Exercise 12.1 - Case Study Using Dataset D: Occupation Description -- Analysis using R -- Exercise 12.2 - Case D: Resume and Job Description -- Reference -- Chapter 13 : Analysis of Large Datasets by Sampling -- Using Sampling to Work with Large Data Files -- Exercise 13.1 - Big Data Analysis -- Additional Case Study Using Dataset E: BankComplaints Big Data File -- Chapter 14 : Installing R and RStudio -- Installing R -- Install R Software for a Mac System -- Installing RStudio -- Reference -- Chapter 15 : Installing the Entity Extraction Tool -- Downloading and Installing the Tool -- The NER Graphical User Interface -- Reference -- Chapter 16 : Installing the Topic Modeling Tool. 327 $aInstalling and Using the Topic Modeling Tool -- Install the tool -- For Macs -- For Windows PCs -- UTF-8 caveat -- Setting up the workspace -- Workspace Directory -- Using the Tool -- Select metadata file -- Selecting the number of topics -- Analyzing the Output -- Multiple Passes for Optimization -- The Output Files -- Chapter 17 : Installing the Voyant Text Analysis Tool -- Install or Update Java -- Installation of Voyant Server -- The Voyant Server -- Downloading VoyantServer -- Running Voyant Server -- Controlling the Voyant Server -- Testing the Installation -- Reference -- INDEX. 330 $aWith the rise in data science development, we now have many remarkable techniques and tools to extend data analysis from numeric and categorical data to textual data. Sifting through the open-ended responses from a survey, for example, was an arduous process when performed by hand. Using a case study approach, this book was written for business analysts who wish to increase their skills in extracting answers for text data in order to support business decision making. Most of the exercises use Excel, today?s most common analysis tool, and R, a popular analytic computer environment. The techniques covered range from the most basic text analytics, such as key word analysis, to more sophisticated techniques, such as topic extraction and text similarity scoring. Companion files with numerous datasets are included for use with case studies and exercises. FEATURES: Organized by tool or technique, with the basic techniques presented first and the more sophisticated techniques presented laterUses Excel and R for datasets in case studies and exercisesFeatures the CRISP-DM data mining standard with early chapters for conducting the preparatory steps in data miningCompanion files with numerous datasets and figures from the text.The companion files are available online by emailing the publisher with proof of purchase at info@merclearning.com. 606 $aBusiness$xDecision making$xComputer programs 606 $aText processing (Computer science) 610 $aBusiness Communication. 610 $aComputer Science. 610 $aData Analytics. 610 $aData Visualization. 610 $aExcel. 610 $aR. 615 0$aBusiness$xDecision making$xComputer programs. 615 0$aText processing (Computer science) 676 $a650.02855369 700 $aFortino$b Andres G.$01143445 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910806102003321 996 $aText analytics for business decisions$94036127 997 $aUNINA LEADER 00974oas 22003493 450 001 9910891415803321 005 20250718213021.0 035 $a(DE-599)ZDB2724984-0 035 $a(OCoLC)828513471 035 $a(CONSER) 2013201413 035 $a(CKB)2550000001047997 035 $a(EXLCZ)992550000001047997 100 $a20130226a20129999 uy a 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aScience journal of circuits, systems and signal processing 210 1$aNew York, NY :$cScience Publishing Group,$d2012- 311 08$a2326-9065 517 3 $aCSSP 531 0 $aSci. j. circuits syst. signal process 676 $a621.3192 801 0$bDLC 801 1$bDLC 801 2$bOCLCQ 801 2$bOCLCL 906 $aJOURNAL 912 $a9910891415803321 996 $aScience journal of circuits, systems and signal processing$94244707 997 $aUNINA