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Applied Text Analysis with Python

Book Description

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 sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning.

You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems.

  • Preprocess and vectorize text into high-dimensional feature representations
  • Perform document classification and topic modeling
  • Steer the model selection process with visual diagnostics
  • Extract key phrases, named entities, and graph structures to reason about data in text
  • Build a dialog framework to enable chatbots and language-driven interaction
  • Use Spark to scale processing power and neural networks to scale model complexity

Table of Contents

  1. Preface
    1. Computational Challenges of Natural Language
      1. Linguistic Data: Tokens and Words
      2. Enter Machine Learning
    2. Tools for Text Analysis
    3. What to Expect from This Book
    4. Who This Book Is For
    5. Code Examples and GitHub Repository
    6. Conventions Used in This Book
    7. Using Code Examples
    8. O’Reilly Safari
    9. How to Contact Us
    10. Acknowledgments
  2. 1. Language and Computation
    1. The Data Science Paradigm
    2. Language-Aware Data Products
      1. The Data Product Pipeline
    3. Language as Data
      1. A Computational Model of Language
      2. Language Features
      3. Contextual Features
      4. Structural Features
    4. Conclusion
  3. 2. Building a Custom Corpus
    1. What Is a Corpus?
      1. Domain-Specific Corpora
      2. The Baleen Ingestion Engine
    2. Corpus Data Management
      1. Corpus Disk Structure
    3. Corpus Readers
      1. Streaming Data Access with NLTK
      2. Reading an HTML Corpus
      3. Reading a Corpus from a Database
    4. Conclusion
  4. 3. Corpus Preprocessing and Wrangling
    1. Breaking Down Documents
      1. Identifying and Extracting Core Content
      2. Deconstructing Documents into Paragraphs
      3. Segmentation: Breaking Out Sentences
      4. Tokenization: Identifying Individual Tokens
      5. Part-of-Speech Tagging
      6. Intermediate Corpus Analytics
    2. Corpus Transformation
      1. Intermediate Preprocessing and Storage
      2. Reading the Processed Corpus
    3. Conclusion
  5. 4. Text Vectorization and Transformation Pipelines
    1. Words in Space
      1. Frequency Vectors
      2. One-Hot Encoding
      3. Term Frequency–Inverse Document Frequency
      4. Distributed Representation
    2. The Scikit-Learn API
      1. The BaseEstimator Interface
      2. Extending TransformerMixin
    3. Pipelines
      1. Pipeline Basics
      2. Grid Search for Hyperparameter Optimization
      3. Enriching Feature Extraction with Feature Unions
    4. Conclusion
  6. 5. Classification for Text Analysis
    1. Text Classification
      1. Identifying Classification Problems
      2. Classifier Models
    2. Building a Text Classification Application
      1. Cross-Validation
      2. Model Construction
      3. Model Evaluation
      4. Model Operationalization
    3. Conclusion
  7. 6. Clustering for Text Similarity
    1. Unsupervised Learning on Text
    2. Clustering by Document Similarity
      1. Distance Metrics
      2. Partitive Clustering
      3. Hierarchical Clustering
    3. Modeling Document Topics
      1. Latent Dirichlet Allocation
      2. Latent Semantic Analysis
      3. Non-Negative Matrix Factorization
    4. Conclusion
  8. 7. Context-Aware Text Analysis
    1. Grammar-Based Feature Extraction
      1. Context-Free Grammars
      2. Syntactic Parsers
      3. Extracting Keyphrases
      4. Extracting Entities
    2. n-Gram Feature Extraction
      1. An n-Gram-Aware CorpusReader
      2. Choosing the Right n-Gram Window
      3. Significant Collocations
    3. n-Gram Language Models
      1. Frequency and Conditional Frequency
      2. Estimating Maximum Likelihood
      3. Unknown Words: Back-off and Smoothing
      4. Language Generation
    4. Conclusion
  9. 8. Text Visualization
    1. Visualizing Feature Space
      1. Visual Feature Analysis
      2. Guided Feature Engineering
    2. Model Diagnostics
      1. Visualizing Clusters
      2. Visualizing Classes
      3. Diagnosing Classification Error
    3. Visual Steering
      1. Silhouette Scores and Elbow Curves
    4. Conclusion
  10. 9. Graph Analysis of Text
    1. Graph Computation and Analysis
      1. Creating a Graph-Based Thesaurus
      2. Analyzing Graph Structure
      3. Visual Analysis of Graphs
    2. Extracting Graphs from Text
      1. Creating a Social Graph
      2. Insights from the Social Graph
    3. Entity Resolution
      1. Entity Resolution on a Graph
      2. Blocking with Structure
      3. Fuzzy Blocking
    4. Conclusion
  11. 10. Chatbots
    1. Fundamentals of Conversation
      1. Dialog: A Brief Exchange
      2. Maintaining a Conversation
    2. Rules for Polite Conversation
      1. Greetings and Salutations
      2. Handling Miscommunication
    3. Entertaining Questions
      1. Dependency Parsing
      2. Constituency Parsing
      3. Question Detection
      4. From Tablespoons to Grams
    4. Learning to Help
      1. Being Neighborly
      2. Offering Recommendations
    5. Conclusion
  12. 11. Scaling Text Analytics with Multiprocessing and Spark
    1. Python Multiprocessing
      1. Running Tasks in Parallel
      2. Process Pools and Queues
      3. Parallel Corpus Preprocessing
    2. Cluster Computing with Spark
      1. Anatomy of a Spark Job
      2. Distributing the Corpus
      3. RDD Operations
      4. NLP with Spark
    3. Conclusion
  13. 12. Deep Learning and Beyond
    1. Applied Neural Networks
    2. Neural Language Models
      1. Artificial Neural Networks
      2. Deep Learning Architectures
    3. Sentiment Analysis
      1. Deep Structure Analysis
    4. The Future Is (Almost) Here
  14. Glossary
  15. Index