Text Analytics (Record no. 367774)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03891 a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250924132351.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250912b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781032794013 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Atkinson-Abutridy J |
| 9 (RLIN) | 213162 |
| 245 ## - TITLE STATEMENT | |
| Title | Text Analytics |
| Remainder of title | Introduction Science Applications Unstructured Information Analysis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Apress |
| Date of publication, distribution, etc. | 2022 |
| Place of publication, distribution, etc. | Boca Raton FL |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 230pp, |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Text Analytics: An Introduction to the Science and Applications of Unstructured Information Analysis is a concise and accessible introduction to the science and applications of text analytics (or text mining), which enables automatic knowledge discovery from unstructured information sources, for both industrial and academic purposes. The book introduces the main concepts, models, and computational techniques that enable the reader to solve real decision-making problems arising from textual and/or documentary sources.<br/><br/>Features:<br/><br/><br/>Easy-to-follow step-by-step concepts and methods<br/><br/>Every chapter is introduced in a very gentle and intuitive way so students can understand the WHYs, WHAT-IFs, WHAT-IS-THIS-FORs, HOWs, etc. by themselves<br/><br/>Practical programming exercises in Python for each chapter<br/><br/>Includes theory and practice for every chapter, summaries, practical coding exercises for target problems, QA, and sample code and data available for download at https://www.routledge.com/Atkinson-Abutridy/p/book/9781032249797<br/>1 TEXT ANALYTICS. 1.1 INTRODUCTION 1.2 TEXT MINING AND TEXT ANALYTICS 1.3 TASKS AND APPLICATIONS 1.4 THE TEXT ANALYTICS PROCESS 1.5 SUMMARY 1.6 QUESTIONS 2 NATURAL-LANGUAGE PROCESSING 2.1 INTRODUCTION 2.2 THE SCOPE OF NATURAL-LANGUAGE PROCESSING 2.3 NLP LEVELS AND TASKS 2.3.1 Phonology 2.3.2 Morphology 2.3.3 Lexicon 2.3.4 Syntax 2.3.5 Semantic 2.3.6 Reasoning and Pragmatics 2.1 SUMMARY 2.2 EXERCISES 2.2.1 Morphological Analysis 2.2.2 Lexical Analysis 2.2.3 Syntactic Analysis 3 INFORMATION EXTRACTION 3.1 INTRODUCTION 3.2 RULE-BASED INFORMATION EXTRACTION 3.3 NAMED-ENTITY RECOGNITION 3.3.1 N-Gram Models 3.4 RELATION EXTRACTION 3.5 EVALUATION 3.1 SUMMARY 3.2 EXERCISE 3.2.1 Regular Expressions 3.2.2 Named-Entity Recognition 4 DOCUMENT REPRESENTATION 4.1 INTRODUCTION 4.2 DOCUMENT INDEXING 4.3 VECTOR SPACE MODELS 4.3.1 Boolean Representation Model 4.3.2 Term Frequency Model 4.3.3 Inverse Document Frequency Model 4.1 SUMMARY 4.2 EXERCISES 4.2.1 TFxIDF Representation Model 5 ASSOCIATION RULES MINING 5. INTRODUCTION 5.2 ASSOCIATION PATTERNS 5.3 EVALUATION 5.3.1 Support 5.3.2Confidence 5.3.3 Lift 5.4 ASSOCIATION RULES GENERATION 5.1 SUMMARY 5.2 EXERCISES 5.2.1 Extraction of Association Rules 6 CORPUS-BASED SEMANTIC ANALYSIS 6.1 INTRODUCTION 6.2 CORPUS-BASED SEMANTIC ANALYSIS 6.3 LATENT SEMANTIC ANALYSIS 6.3.1 Creating Vectors with LSA 6.4 WORD2VEC 6.4.1 Embedding Learning 6.4.2 Prediction and Embeddings Interpretation 6.1 SUMMARY 6.2 EXERCISES 6.2.1 Latent Semantic Analysis 6.2. Word Embedding with Word2Vec 7 DOCUMENT CLUSTERING 7.1 INTRODUCTION 7.2 DOCUMENT CLUSTERING 7.3K-MEANS CLUSTERING 7.4 SELF-ORGANIZING MAP 7.4.1Topological Maps Learning 7.1 SUMMARY 7.2 EXERCISES 7.2.1 K-means Clustering 7.2.2 Self-Organizing Maps 8 TOPIC MODELING 8.1 INTRODUCTIO 8.2TOPIC MODELING 8.3 LATENT DIRICHLET ALLOCATION 8.4 EVALUATION 8.1 SUMMARY 8.2 EXERCISES 8.2.1 Modeling Topics with LDA 9 DOCUMENT CATEGORIZATION 9.1INTRODUCTION 9.2 CATEGORIZATION MODELS 9.3 BAYESIAN TEXT CATEGORIZATION 9.4 MAXIMUM ENTROPY CATEGORIZATION 9.5 EVALUATION 9.1 SUMMARY 9.2 EXERCISES 9.2.1 Naïve Bayes Categorization 9.2.2 MaxEnt Categorization |
| 650 10 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Development Systems & Control Engineering |
| 9 (RLIN) | 213369 |
| 650 20 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Engineering & Technology Statistics |
| 9 (RLIN) | 213370 |
| 650 30 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Statistics & Probability |
| 9 (RLIN) | 213371 |
| 650 40 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Mathematics & Statistics |
| 9 (RLIN) | 213372 |
| 650 50 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Mining and Knowledge |
| 9 (RLIN) | 213373 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Date acquired | Vendor | Cost, normal purchase price | Total Checkouts | Barcode | Date last seen | Cost, replacement price | Bill Date | Koha item type | Original Barcode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | MCA | MKSSS s K.B. Joshi Institute of Information Technology Library | MKSSS s K.B. Joshi Institute of Information Technology Library | 11/09/2025 | 87 | 1346.25 | KBJP-BK-2828 | 11/09/2025 | 1795.00 | 11/09/2025 | Books | 2828 |
