Practical text analytics: maximizing the value of text data
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer Nature Switzerland AG
[2019]
|
| Schriftenreihe: | Advances in analytics and data science
volume 2 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxviii, 285 Seiten Illustrationen, Diagramme |
| ISBN: | 9783319956626 |
Internformat
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| 245 | 1 | 0 | |a Practical text analytics |b maximizing the value of text data |c Murugan Anandarajan, Chelsey Hill, Thomas Nolan |
| 264 | 1 | |a Cham |b Springer Nature Switzerland AG |c [2019] | |
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| 650 | 4 | |a Business Information Systems | |
| 650 | 4 | |a Statistics for Business/Economics/Mathematical Finance/Insurance | |
| 650 | 4 | |a Big data | |
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| 700 | 1 | |a Hill, Chelsey |e Verfasser |4 aut | |
| 700 | 1 | |a Nolan, Thomas |e Verfasser |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-319-95663-3 |
| 830 | 0 | |a Advances in analytics and data science |v volume 2 |w (DE-604)BV045304301 |9 2 | |
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Datensatz im Suchindex
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| adam_text | Contents 1 Introduction to Text Analytics............................................................... 1.1 Introduction....................................................................................... 1.2 Text Analytics: What Is It?............................................................... 1.3 Origins and Timeline of Text Analytics........................................... 1.4 Text Analytics in Business and Industry......................................... 1.5 Text Analytics Skills......................................................................... 1.6 Benefits of Text Analytics............................................................... 1.7 Text Analytics Process Road Map................................................... 1.7.1 Planning................................................................................ 1.7.2 Text Preparing and Preprocessing..................................... 1.7.3 Text Analysis Techniques..................................................... 1.7.4 Communicating the Results................................................. 1.8 Examples of Text Analytics Software............................................ References................................................................................................... Part I 2 1 1 1 3 5 6 8 8 8 9 9 10 10 10 Planning the Text Analytics Project The Fundamentals of Content Analysis............................................... 2.1 Introduction....................................................................................... 2.2 Deductive Versus Inductive
Approaches........................................ 2.2.1 Content Analysis for Deductive Inference......................... 2.2.2 Content Analysis for Inductive Inference......................... 2.3 Unitizing and the Unit of Analysis................................................... 2.3.1 The Sampling Unit............................................................... 2.3.2 The Recording Unit............................................................. 2.3.3 The Context Unit.................................................................. 2.4 Sampling........................................................................................... 2.5 Coding and Categorization............................................................. 2.6 Examples of Inductive and Deductive Inference Processes........... 2.6.1 Inductive Inference............................................................. 15 15 16 16 16 18 19 19 20 20 20 23 23 xi
Contents xii 2.6.2 Deductive Inference............................................................... References..................................................................................................... 3 Planning for Text Analytics 3.1 Introduction........................................................................................ 3.2 Initial Planning Considerations......................................................... 3.2.1 Drivers.................................................................................... 3.2.2 Objectives................................................................................ 3.2.3 Data........................................................................................ 3.2.4 Cost........................................................................................ 3.3 Planning Process................................................................................ 3.4 Problem Framing................................................................................ 3.4.1 Identifying the Analysis Problem......................................... 3.4.2 Inductive or Deductive Inference......................................... 3.5 Data Generation.................................................................................. 3.5.1 Definition of the Project’s Scope and Purpose.................... 3.5.2 Text Data Collection............................................................ 3.5.3 Sampling............................................................................... 3.6 Method and Implementation
Selection........................................... 3.6.1 Analysis Method Selection.................................................. 3.6.2 The Selection of Implementation Software......................... References.................................................................................................... Part II 24 25 27 27 28 29 29 30 30 30 31 31 32 32 32 33 35 38 38 39 40 Text Preparation 4 Text Preprocessing................................................................................... 4.1 Introduction....................................................................................... 4.2 The Preprocessing Process................................................................ 4.3 Unitize and Tokenize........................................................................ 4.3.1 N-Grams................................................................................. 4.4 Standardization and Cleaning......................................................... 4.5 Stop Word Removal.......................................................................... 4.5.1 Custom Stop Word Dictionaries........................................ 4.6 Stemming and Lemmatization......................................................... 4.6.1 Syntax and Semantics......................................................... 4.6.2 Stemming.............................................................................. 4.6.3 Lemmatization...................................................................... 4.6.4 Part-of-Speech (POS) Tagging.............................................
References................................................................................................... 45 45 46 46 48 50 50 53 53 53 54 56 58 59 5 Term-Document Representation........................................................... 5.1 Introduction....................................................................................... 5.2 The Inverted Index............................................................................ 5.3 The Term-Document Matrix........................................................... 5.4 Term-Document Matrix Frequency Weighting.............................. 5.4.1 Local Weighting.................................................................... 61 61 61 64 65 66
xiii Contents 5.4.2 Global Weighting................................................................. 5.4.3 Combinatorial Weighting: Local and Global Weighting .. 5.5 Decision-Making............................................................................. References................................................................................................... Part III 6 68 71 73 73 Text Analysis Techniques Semantic Space Representation and Latent Semantic Analysis..................................................................................................... 6.1 Introduction...................................................................................... 6.2 Latent Semantic Analysis (LSA).................................................... 6.2.1 Singular Value Decomposition (SYD)............................... 6.2.2 LSA Example....................................................................... 6.3 Cosine Similarity.............................................................................. 6.4 Queries in LSA................................................................................ 6.5 Decision-Making: Choosing the Number of Dimensions............. References................................................................................................... 77 77 79 79 80 84 87 88 91 7 Cluster Analysis: Modeling Groups in Text........................................ 7.1 Introduction...................................................................................... 7.2 Distance and
Similarity................................................................... 7.3 Hierarchical Cluster Analysis......................................................... 7.3.1 Hierarchical Cluster Analysis Algorithm........................... 7.3.2 Graph Methods..................................................................... 7.3.3 Geometric Methods............................................................. 7.3.4 Advantages and Disadvantages of HCA........................... 7.4 ł-Means Clustering.......................................................................... 7.4.1 kMC Algorithm................................................................... 7.4.2 The kMC Process................................................................. 7.4.3 Advantages and Disadvantages of kMC........................... 7.5 Cluster Analysis: Model Fit and Decision-Making....................... 7.5.1 Choosing the Number of Clusters...................................... 7.5.2 Naming/Describing Clusters.............................................. 7.5.3 Evaluating Model Fit........................................................... 7.5.4 Choosing the Cluster Analysis Model............................... References................................................................................................... 93 93 94 98 99 99 101 103 103 104 104 108 109 109 112 113 114 115 8 Probabilistic Topic Models..................................................................... 8.1 Introduction....................................................................................... 8.2
Latent Dirichlet Allocation (LDA).................................................. 8.3 Correlated Topic Model (CTM)....................................................... 8.4 Dynamic Topic Model (DT)........................................................... 8.5 Supervised Topic Model (sLDA)..................................................... 8.6 Structural Topic Model (STM)....................................................... 8.7 Decision Making in Topic Models.................................................. 8.7.1 Assessing Model Fit and Number of Topics..................... 117 117 119 120 122 123 124 124 124
xiv 9 10 Contents 8.7.2 Model Validation and Topic Identification.......................... 8.7.3 When to Use Topic Models.................................................. References...................................................................................................... 126 128 129 Classification Analysis: MachineLearning Applied to Text............... 9.1 Introduction......................................................................................... 9.2 The General Text Classification Process.......................................... 9.3 Evaluating Model Fit.......................................................................... 9.3.1 Confusion Matrices/Contingency Tables............................. 9.3.2 Overall Model Measures...................................................... 9.3.3 Class-Specific Measures...................................................... 9.4 Classification Models.......................................................................... 9.4.1 Naive Bayes............................................................................ 9.4.2 ^-Nearest Neighbors (kNN).................................................. 9.4.3 Support Vector Machines (SVM)......................................... 9.4.4 Decision Trees....................................................................... 9.4.5 Random Forests..................................................................... 9.4.6 Neural Networks................................................................... 9.5 Choosing a
Classification................................................................... 9.5.1 Model Fit............................................................................... References.................................................................................................... 131 131 132 132 132 134 135 137 137 138 140 141 143 144 146 146 148 Modeling Text Sentiment: Learning and Lexicon Models............... 10.1 Lexicon Approach........................................................................... 10.2 Machine Learning Approach.......................................................... 10.2.1 Naïve Bayes (NB).............................................................. 10.2.2 Support Vector Machines (SVM)........................................ 10.2.3 Logistic Regression............................................................ 10.3 Sentiment Analysis Performance: Considerations and Evaluation................................................................................. References.................................................................................................... 151 152 158 159 160 161 Part IV 11 162 164 Communicating the Results Storytelling Using Text Data.................................................................. 11.1 Introduction..................................................................................... 11.2 Telling Stories About the Data........................................................ 11.3 Framing the Story.......................................................................... 11.3.1 Storytelling
Framework..................................................... 11.3.2 Applying the Framework................................................... 11.4 Organizations as Storytellers.......................................................... 11.4.1 United Parcel Service.......................................................... 11.4.2 Zillow................................................................................... 11.5 Data Storytelling Checklist............................................................ References.................................................................................................... 167 167 168 170 170 171 173 173 173 174 175
Contents 12 XV Visualizing Analysis Results.................................................................... 12.1 Strategies for Effective Visualization............................................ 12.1.1 Be Purposeful...................................................................... 12.1.2 Know the Audience........................................................... 12.1.3 Solidify the Message......................................................... 12.1.4 Plan and Outline................................................................. 12.1.5 Keep It Simple................................................................... 12.1.6 Focus Attention................................................................. 12.2 Visualization Techniques in Text Analytics................................ 12.2.1 Corpus/Document Collection-Level Visualizations........ 12.2.2 Theme and Category-Level Visualizations..................... 12.2.3 Document-Level Visualizations........................................ References................................................................................................... Part V 177 178 178 178 178 179 179 180 180 180 182 188 190 Text Analytics Examples 13 Sentiment Analysis of Movie Reviews Using R.................................. 13.1 Introduction to R and RStudio....................................................... 13.2 SA Data and Data Import............................................................... 13.3 Objective of the Sentiment Analysis............................................ 13.4 Data Preparation and
Preprocessing............................................ 13.4.1 Tokenize.............................................................................. 13.4.2 Remove Stop Words........................................................... 13.5 Sentiment Analysis.......................................................................... 13.6 Sentiment Analysis Results........................................................... 13.7 Custom Dictionary.......................................................................... 13.8 Out-of-Sample Comparison........................................................... References................................................................................................... 193 193 194 197 199 199 200 201 204 206 217 219 14 Latent Semantic Analysis (LSA) in Python........................................ 14.1 Introduction to Python and IDLE................................................. 14.2 Preliminary Steps............................................................................ 14.3 Getting Started................................................................................ 14.4 Data and Data Import...................................................................... 14.5 Analysis........................................................................................... Further Reading......................................................................................... 221 221 222 224 225 227 242 15 Learning-Based Sentiment Analysis Using RapidMiner................... 15.1
Introduction.................................................................................... 15.2 Getting Started in RapidMiner....................................................... 15.3 Text Data Import............................................................................ 15.4 Text Preparation and Preprocessing.............................................. 15.5 Text Classification Sentiment Analysis........................................ Reference................................................................................................... 243 243 244 247 249 256 261
xvi 16 Contents SAS Visual Text Analytics......................................................................... 16.1 Introduction....................................................................................... 16.2 Getting Started.................................................................................. 16.3 Analysis............................................................................................. Further Reading........................................................................................... 263 263 264 266 282 Index...................................................................................................................... 283
|
| adam_txt |
Contents 1 Introduction to Text Analytics. 1.1 Introduction. 1.2 Text Analytics: What Is It?. 1.3 Origins and Timeline of Text Analytics. 1.4 Text Analytics in Business and Industry. 1.5 Text Analytics Skills. 1.6 Benefits of Text Analytics. 1.7 Text Analytics Process Road Map. 1.7.1 Planning. 1.7.2 Text Preparing and Preprocessing. 1.7.3 Text Analysis Techniques. 1.7.4 Communicating the Results. 1.8 Examples of Text Analytics Software. References. Part I 2 1 1 1 3 5 6 8 8 8 9 9 10 10 10 Planning the Text Analytics Project The Fundamentals of Content Analysis. 2.1 Introduction. 2.2 Deductive Versus Inductive
Approaches. 2.2.1 Content Analysis for Deductive Inference. 2.2.2 Content Analysis for Inductive Inference. 2.3 Unitizing and the Unit of Analysis. 2.3.1 The Sampling Unit. 2.3.2 The Recording Unit. 2.3.3 The Context Unit. 2.4 Sampling. 2.5 Coding and Categorization. 2.6 Examples of Inductive and Deductive Inference Processes. 2.6.1 Inductive Inference. 15 15 16 16 16 18 19 19 20 20 20 23 23 xi
Contents xii 2.6.2 Deductive Inference. References. 3 Planning for Text Analytics 3.1 Introduction. 3.2 Initial Planning Considerations. 3.2.1 Drivers. 3.2.2 Objectives. 3.2.3 Data. 3.2.4 Cost. 3.3 Planning Process. 3.4 Problem Framing. 3.4.1 Identifying the Analysis Problem. 3.4.2 Inductive or Deductive Inference. 3.5 Data Generation. 3.5.1 Definition of the Project’s Scope and Purpose. 3.5.2 Text Data Collection. 3.5.3 Sampling. 3.6 Method and Implementation
Selection. 3.6.1 Analysis Method Selection. 3.6.2 The Selection of Implementation Software. References. Part II 24 25 27 27 28 29 29 30 30 30 31 31 32 32 32 33 35 38 38 39 40 Text Preparation 4 Text Preprocessing. 4.1 Introduction. 4.2 The Preprocessing Process. 4.3 Unitize and Tokenize. 4.3.1 N-Grams. 4.4 Standardization and Cleaning. 4.5 Stop Word Removal. 4.5.1 Custom Stop Word Dictionaries. 4.6 Stemming and Lemmatization. 4.6.1 Syntax and Semantics. 4.6.2 Stemming. 4.6.3 Lemmatization. 4.6.4 Part-of-Speech (POS) Tagging.
References. 45 45 46 46 48 50 50 53 53 53 54 56 58 59 5 Term-Document Representation. 5.1 Introduction. 5.2 The Inverted Index. 5.3 The Term-Document Matrix. 5.4 Term-Document Matrix Frequency Weighting. 5.4.1 Local Weighting. 61 61 61 64 65 66
xiii Contents 5.4.2 Global Weighting. 5.4.3 Combinatorial Weighting: Local and Global Weighting . 5.5 Decision-Making. References. Part III 6 68 71 73 73 Text Analysis Techniques Semantic Space Representation and Latent Semantic Analysis. 6.1 Introduction. 6.2 Latent Semantic Analysis (LSA). 6.2.1 Singular Value Decomposition (SYD). 6.2.2 LSA Example. 6.3 Cosine Similarity. 6.4 Queries in LSA. 6.5 Decision-Making: Choosing the Number of Dimensions. References. 77 77 79 79 80 84 87 88 91 7 Cluster Analysis: Modeling Groups in Text. 7.1 Introduction. 7.2 Distance and
Similarity. 7.3 Hierarchical Cluster Analysis. 7.3.1 Hierarchical Cluster Analysis Algorithm. 7.3.2 Graph Methods. 7.3.3 Geometric Methods. 7.3.4 Advantages and Disadvantages of HCA. 7.4 ł-Means Clustering. 7.4.1 kMC Algorithm. 7.4.2 The kMC Process. 7.4.3 Advantages and Disadvantages of kMC. 7.5 Cluster Analysis: Model Fit and Decision-Making. 7.5.1 Choosing the Number of Clusters. 7.5.2 Naming/Describing Clusters. 7.5.3 Evaluating Model Fit. 7.5.4 Choosing the Cluster Analysis Model. References. 93 93 94 98 99 99 101 103 103 104 104 108 109 109 112 113 114 115 8 Probabilistic Topic Models. 8.1 Introduction. 8.2
Latent Dirichlet Allocation (LDA). 8.3 Correlated Topic Model (CTM). 8.4 Dynamic Topic Model (DT). 8.5 Supervised Topic Model (sLDA). 8.6 Structural Topic Model (STM). 8.7 Decision Making in Topic Models. 8.7.1 Assessing Model Fit and Number of Topics. 117 117 119 120 122 123 124 124 124
xiv 9 10 Contents 8.7.2 Model Validation and Topic Identification. 8.7.3 When to Use Topic Models. References. 126 128 129 Classification Analysis: MachineLearning Applied to Text. 9.1 Introduction. 9.2 The General Text Classification Process. 9.3 Evaluating Model Fit. 9.3.1 Confusion Matrices/Contingency Tables. 9.3.2 Overall Model Measures. 9.3.3 Class-Specific Measures. 9.4 Classification Models. 9.4.1 Naive Bayes. 9.4.2 ^-Nearest Neighbors (kNN). 9.4.3 Support Vector Machines (SVM). 9.4.4 Decision Trees. 9.4.5 Random Forests. 9.4.6 Neural Networks. 9.5 Choosing a
Classification. 9.5.1 Model Fit. References. 131 131 132 132 132 134 135 137 137 138 140 141 143 144 146 146 148 Modeling Text Sentiment: Learning and Lexicon Models. 10.1 Lexicon Approach. 10.2 Machine Learning Approach. 10.2.1 Naïve Bayes (NB). 10.2.2 Support Vector Machines (SVM). 10.2.3 Logistic Regression. 10.3 Sentiment Analysis Performance: Considerations and Evaluation. References. 151 152 158 159 160 161 Part IV 11 162 164 Communicating the Results Storytelling Using Text Data. 11.1 Introduction. 11.2 Telling Stories About the Data. 11.3 Framing the Story. 11.3.1 Storytelling
Framework. 11.3.2 Applying the Framework. 11.4 Organizations as Storytellers. 11.4.1 United Parcel Service. 11.4.2 Zillow. 11.5 Data Storytelling Checklist. References. 167 167 168 170 170 171 173 173 173 174 175
Contents 12 XV Visualizing Analysis Results. 12.1 Strategies for Effective Visualization. 12.1.1 Be Purposeful. 12.1.2 Know the Audience. 12.1.3 Solidify the Message. 12.1.4 Plan and Outline. 12.1.5 Keep It Simple. 12.1.6 Focus Attention. 12.2 Visualization Techniques in Text Analytics. 12.2.1 Corpus/Document Collection-Level Visualizations. 12.2.2 Theme and Category-Level Visualizations. 12.2.3 Document-Level Visualizations. References. Part V 177 178 178 178 178 179 179 180 180 180 182 188 190 Text Analytics Examples 13 Sentiment Analysis of Movie Reviews Using R. 13.1 Introduction to R and RStudio. 13.2 SA Data and Data Import. 13.3 Objective of the Sentiment Analysis. 13.4 Data Preparation and
Preprocessing. 13.4.1 Tokenize. 13.4.2 Remove Stop Words. 13.5 Sentiment Analysis. 13.6 Sentiment Analysis Results. 13.7 Custom Dictionary. 13.8 Out-of-Sample Comparison. References. 193 193 194 197 199 199 200 201 204 206 217 219 14 Latent Semantic Analysis (LSA) in Python. 14.1 Introduction to Python and IDLE. 14.2 Preliminary Steps. 14.3 Getting Started. 14.4 Data and Data Import. 14.5 Analysis. Further Reading. 221 221 222 224 225 227 242 15 Learning-Based Sentiment Analysis Using RapidMiner. 15.1
Introduction. 15.2 Getting Started in RapidMiner. 15.3 Text Data Import. 15.4 Text Preparation and Preprocessing. 15.5 Text Classification Sentiment Analysis. Reference. 243 243 244 247 249 256 261
xvi 16 Contents SAS Visual Text Analytics. 16.1 Introduction. 16.2 Getting Started. 16.3 Analysis. Further Reading. 263 263 264 266 282 Index. 283 |
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| oclc_num | 1140555708 |
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| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xxviii, 285 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Springer Nature Switzerland AG |
| record_format | marc |
| series | Advances in analytics and data science |
| series2 | Advances in analytics and data science |
| spelling | Anandarajan, Murugan 1961- Verfasser (DE-588)128478624 aut Practical text analytics maximizing the value of text data Murugan Anandarajan, Chelsey Hill, Thomas Nolan Cham Springer Nature Switzerland AG [2019] © 2019 xxviii, 285 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Advances in analytics and data science volume 2 Big Data/Analytics Business Information Systems Statistics for Business/Economics/Mathematical Finance/Insurance Big data Management information systems Statistics Text Mining (DE-588)4728093-1 gnd rswk-swf Text Mining (DE-588)4728093-1 s DE-604 Hill, Chelsey Verfasser aut Nolan, Thomas Verfasser aut Erscheint auch als Online-Ausgabe 978-3-319-95663-3 Advances in analytics and data science volume 2 (DE-604)BV045304301 2 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032966860&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Anandarajan, Murugan 1961- Hill, Chelsey Nolan, Thomas Practical text analytics maximizing the value of text data Advances in analytics and data science Big Data/Analytics Business Information Systems Statistics for Business/Economics/Mathematical Finance/Insurance Big data Management information systems Statistics Text Mining (DE-588)4728093-1 gnd |
| subject_GND | (DE-588)4728093-1 |
| title | Practical text analytics maximizing the value of text data |
| title_auth | Practical text analytics maximizing the value of text data |
| title_exact_search | Practical text analytics maximizing the value of text data |
| title_exact_search_txtP | Practical text analytics maximizing the value of text data |
| title_full | Practical text analytics maximizing the value of text data Murugan Anandarajan, Chelsey Hill, Thomas Nolan |
| title_fullStr | Practical text analytics maximizing the value of text data Murugan Anandarajan, Chelsey Hill, Thomas Nolan |
| title_full_unstemmed | Practical text analytics maximizing the value of text data Murugan Anandarajan, Chelsey Hill, Thomas Nolan |
| title_short | Practical text analytics |
| title_sort | practical text analytics maximizing the value of text data |
| title_sub | maximizing the value of text data |
| topic | Big Data/Analytics Business Information Systems Statistics for Business/Economics/Mathematical Finance/Insurance Big data Management information systems Statistics Text Mining (DE-588)4728093-1 gnd |
| topic_facet | Big Data/Analytics Business Information Systems Statistics for Business/Economics/Mathematical Finance/Insurance Big data Management information systems Statistics Text Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032966860&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV045304301 |
| work_keys_str_mv | AT anandarajanmurugan practicaltextanalyticsmaximizingthevalueoftextdata AT hillchelsey practicaltextanalyticsmaximizingthevalueoftextdata AT nolanthomas practicaltextanalyticsmaximizingthevalueoftextdata |