Applied text analysis with Python enabling language-aware data products with machine learning
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data source...
Main Authors: | , , |
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Format: | eBook |
Language: | English |
Published: |
Sebastopol, CA
O'Reilly Media
2018
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Edition: | First edition |
Subjects: | |
Online Access: | |
Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Syntactic ParsersExtracting Keyphrases; Extracting Entities; n-Gram Feature Extraction; An n-Gram-Aware CorpusReader; Choosing the Right n-Gram Window; Significant Collocations; n-Gram Language Models; Frequency and Conditional Frequency; Estimating Maximum Likelihood; Unknown Words: Back-off and Smoothing; Language Generation; Conclusion; Chapter 8. Text Visualization; Visualizing Feature Space; Visual Feature Analysis; Guided Feature Engineering; Model Diagnostics; Visualizing Clusters; Visualizing Classes; Diagnosing Classification Error; Visual Steering; Silhouette Scores and Elbow Curves
- Structural FeaturesConclusion; Chapter 2. Building a Custom Corpus; What Is a Corpus?; Domain-Specific Corpora; The Baleen Ingestion Engine; Corpus Data Management; Corpus Disk Structure; Corpus Readers; Streaming Data Access with NLTK; Reading an HTML Corpus; Reading a Corpus from a Database; Conclusion; Chapter 3. Corpus Preprocessing and Wrangling; Breaking Down Documents; Identifying and Extracting Core Content; Deconstructing Documents into Paragraphs; Segmentation: Breaking Out Sentences; Tokenization: Identifying Individual Tokens; Part-of-Speech Tagging; Intermediate Corpus Analytics
- Includes bibliographical references and index
- 1. Language and computation
- 2. Building a custom corpus
- 3. Corpus preprocessing and wrangling
- 4. Text vectorization and transformation pipelines
- 5. Classification for text analysis
- 6. Clustering for text similarity
- 7. Context-aware text analysis
- 8. Text visualization
- 9. Graph analysis of text
- 10. Chatbots
- 11. Scaling text analytics with multiprocessing and spark
- 12. Deep learning and beyond
- Identifying Classification ProblemsClassifier Models; Building a Text Classification Application; Cross-Validation; Model Construction; Model Evaluation; Model Operationalization; Conclusion; Chapter 6. Clustering for Text Similarity; Unsupervised Learning on Text; Clustering by Document Similarity; Distance Metrics; Partitive Clustering; Hierarchical Clustering; Modeling Document Topics; Latent Dirichlet Allocation; Latent Semantic Analysis; Non-Negative Matrix Factorization; Conclusion; Chapter 7. Context-Aware Text Analysis; Grammar-Based Feature Extraction; Context-Free Grammars
- Corpus TransformationIntermediate Preprocessing and Storage; Reading the Processed Corpus; Conclusion; Chapter 4. Text Vectorization and Transformation Pipelines; Words in Space; Frequency Vectors; One-Hot Encoding; Term Frequency-Inverse Document Frequency; Distributed Representation; The Scikit-Learn API; The BaseEstimator Interface; Extending TransformerMixin; Pipelines; Pipeline Basics; Grid Search for Hyperparameter Optimization; Enriching Feature Extraction with Feature Unions; Conclusion; Chapter 5. Classification for Text Analysis; Text Classification
- Cover; Copyright; Table of Contents; Preface; Computational Challenges of Natural Language; Linguistic Data: Tokens and Words; Enter Machine Learning; Tools for Text Analysis; What to Expect from This Book; Who This Book Is For; Code Examples and GitHub Repository; Conventions Used in This Book; Using Code Examples; O'Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Language and Computation; The Data Science Paradigm; Language-Aware Data Products; The Data Product Pipeline; Language as Data; A Computational Model of Language; Language Features; Contextual Features