Blueprints for Text Analytics Using Python Jens Albrecht, Sidharth Ramachandran, Christian Winkler

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Spis treści książki

• Preface
  • Approach of the Book
  • Prerequisites
  • Some Important Libraries to Know
  • Books to Read
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Gaining Early Insights from Textual Data
  • What Youll Learn and What Well Build
  • Exploratory Data Analysis
  • Introducing the Dataset
  • Blueprint: Getting an Overview of the Data with Pandas
    • Calculating Summary Statistics for Columns
    • Checking for Missing Data
    • Plotting Value Distributions
    • Comparing Value Distributions Across Categories
    • Visualizing Developments Over Time
  • Blueprint: Building a Simple Text Preprocessing Pipeline
    • Performing Tokenization with Regular Expressions
    • Treating Stop Words
    • Processing a Pipeline with One Line of Code
  • Blueprints for Word Frequency Analysis
    • Blueprint: Counting Words with a Counter
    • Blueprint: Creating a Frequency Diagram
    • Blueprint: Creating Word Clouds
    • Blueprint: Ranking with TF-IDF
  • Blueprint: Finding a Keyword-in-Context
  • Blueprint: Analyzing N-Grams
  • Blueprint: Comparing Frequencies Across Time Intervals and Categories
    • Creating Frequency Timelines
    • Creating Frequency Heatmaps
  • Closing Remarks
• 2. Extracting Textual Insights with APIs
  • What Youll Learn and What Well Build
  • Application Programming Interfaces
  • Blueprint: Extracting Data from an API Using the Requests Module
    • Pagination
    • Rate Limiting
  • Blueprint: Extracting Twitter Data with Tweepy
    • Obtaining Credentials
    • Installing and Configuring Tweepy
    • Extracting Data from the Search API
    • Extracting Data from a Users Timeline
    • Extracting Data from the Streaming API
  • Closing Remarks
• 3. Scraping Websites and Extracting Data
  • What Youll Learn and What Well Build
  • Scraping and Data Extraction
  • Introducing the Reuters News Archive
  • URL Generation
  • Blueprint: Downloading and Interpreting robots.txt
  • Blueprint: Finding URLs from sitemap.xml
  • Blueprint: Finding URLs from RSS
  • Downloading Data
  • Blueprint: Downloading HTML Pages with Python
  • Blueprint: Downloading HTML Pages with wget
  • Extracting Semistructured Data
  • Blueprint: Extracting Data with Regular Expressions
  • Blueprint: Using an HTML Parser for Extraction
    • Extracting the title/headline
    • Extracting the article text
    • Extracting image captions
    • Extracting the URL
    • Extracting list information (authors)
    • Semantic and nonsemantic content
    • Extracting text of links (section)
    • Extracting reading time
    • Extracting attributes (ID)
    • Extracting attribution
    • Extracting timestamp
  • Blueprint: Spidering
    • Introducing the Use Case
    • Error Handling and Production-Quality Software
  • Density-Based Text Extraction
    • Extracting Reuters Content with Readability
    • Summary Density-Based Text Extraction
  • All-in-One Approach
  • Blueprint: Scraping the Reuters Archive with Scrapy
  • Possible Problems with Scraping
  • Closing Remarks and Recommendation
• 4. Preparing Textual Data for Statistics and Machine Learning
  • What Youll Learn and What Well Build
  • A Data Preprocessing Pipeline
  • Introducing the Dataset: Reddit Self-Posts
    • Loading Data Into Pandas
    • Blueprint: Standardizing Attribute Names
    • Saving and Loading a DataFrame
  • Cleaning Text Data
    • Blueprint: Identify Noise with Regular Expressions
    • Blueprint: Removing Noise with Regular Expressions
    • Blueprint: Character Normalization with textacy
    • Blueprint: Pattern-Based Data Masking with textacy
  • Tokenization
    • Blueprint: Tokenization with Regular Expressions
    • Tokenization with NLTK
    • Recommendations for Tokenization
  • Linguistic Processing with spaCy
    • Instantiating a Pipeline
    • Processing Text
    • Blueprint: Customizing Tokenization
    • Blueprint: Working with Stop Words
    • Blueprint: Extracting Lemmas Based on Part of Speech
    • Blueprint: Extracting Noun Phrases
    • Blueprint: Extracting Named Entities
  • Feature Extraction on a Large Dataset
    • Blueprint: Creating One Function to Get It All
    • Blueprint: Using spaCy on a Large Dataset
    • Persisting the Result
    • A Note on Execution Time
  • There Is More
    • Language Detection
    • Spell-Checking
    • Token Normalization
  • Closing Remarks and Recommendations
• 5. Feature Engineering and Syntactic Similarity
  • What Youll Learn and What Well Build
  • A Toy Dataset for Experimentation
  • Blueprint: Building Your Own Vectorizer
    • Enumerating the Vocabulary
    • Vectorizing Documents
      • Out-of-vocabulary documents
    • The Document-Term Matrix
      • Calculating similarities
    • The Similarity Matrix
  • Bag-of-Words Models
    • Blueprint: Using scikit-learns CountVectorizer
      • Fitting the vocabulary
      • Transforming the documents to vectors
    • Blueprint: Calculating Similarities
  • TF-IDF Models
    • Optimized Document Vectors with TfidfTransformer
    • Introducing the ABC Dataset
    • Blueprint: Reducing Feature Dimensions
      • Removing stop words
      • Minimum frequency
      • Maximum frequency
    • Blueprint: Improving Features by Making Them More Specific
      • Performing linguistic analysis
    • Blueprint: Using Lemmas Instead of Words for Vectorizing Documents
    • Blueprint: Limit Word Types
    • Blueprint: Remove Most Common Words
    • Blueprint: Adding Context via N-Grams
      • Options of TfidfVectorizer
        • Think very carefully about feature dimensions
        • Keep number of dimensions in mind
  • Syntactic Similarity in the ABC Dataset
    • Blueprint: Finding Most Similar Headlines to a Made-up Headline
    • Blueprint: Finding the Two Most Similar Documents in a Large Corpus (Much More Difficult)
    • Blueprint: Finding Related Words
    • Tips for Long-Running Programs like Syntactic Similarity
  • Summary and Conclusion
• 6. Text Classification Algorithms
  • What Youll Learn and What Well Build
  • Introducing the Java Development Tools Bug Dataset
  • Blueprint: Building a Text Classification System
    • Step 1: Data Preparation
    • Step 2: Train-Test Split
    • Step 3: Training the Machine Learning Model
    • Step 4: Model Evaluation
      • Precision and recall
      • Class imbalance
  • Final Blueprint for Text Classification
  • Blueprint: Using Cross-Validation to Estimate Realistic Accuracy Metrics
  • Blueprint: Performing Hyperparameter Tuning with Grid Search
  • Blueprint Recap and Conclusion
  • Closing Remarks
  • Further Reading
• 7. How to Explain a Text Classifier
  • What Youll Learn and What Well Build
  • Blueprint: Determining Classification Confidence Using Prediction Probability
  • Blueprint: Measuring Feature Importance of Predictive Models
  • Blueprint: Using LIME to Explain the Classification Results
  • Blueprint: Using ELI5 to Explain the Classification Results
  • Blueprint: Using Anchor to Explain the Classification Results
    • Using the Distribution with Masked Words
    • Working with Real Words
  • Closing Remarks
• 8. Unsupervised Methods: Topic Modeling and Clustering
  • What Youll Learn and What Well Build
  • Our Dataset: UN General Debates
    • Checking Statistics of the Corpus
    • Preparations
  • Nonnegative Matrix Factorization (NMF)
    • Blueprint: Creating a Topic Model Using NMF for Documents
    • Blueprint: Creating a Topic Model for Paragraphs Using NMF
  • Latent Semantic Analysis/Indexing
    • Blueprint: Creating a Topic Model for Paragraphs with SVD
  • Latent Dirichlet Allocation
    • Blueprint: Creating a Topic Model for Paragraphs with LDA
    • Blueprint: Visualizing LDA Results
  • Blueprint: Using Word Clouds to Display and Compare Topic Models
  • Blueprint: Calculating Topic Distribution of Documents and Time Evolution
  • Using Gensim for Topic Modeling
    • Blueprint: Preparing Data for Gensim
    • Blueprint: Performing Nonnegative Matrix Factorization with Gensim
    • Blueprint: Using LDA with Gensim
    • Blueprint: Calculating Coherence Scores
    • Blueprint: Finding the Optimal Number of Topics
    • Blueprint: Creating a Hierarchical Dirichlet Process with Gensim
  • Blueprint: Using Clustering to Uncover the Structure of Text Data
  • Further Ideas
  • Summary and Recommendation
  • Conclusion
• 9. Text Summarization
  • What Youll Learn and What Well Build
  • Text Summarization
    • Extractive Methods
    • Data Preprocessing
  • Blueprint: Summarizing Text Using Topic Representation
    • Identifying Important Words with TF-IDF Values
    • LSA Algorithm
  • Blueprint: Summarizing Text Using an Indicator Representation
  • Measuring the Performance of Text Summarization Methods
  • Blueprint: Summarizing Text Using Machine Learning
    • Step 1: Creating Target Labels
    • Step 2: Adding Features to Assist Model Prediction
    • Step 3: Build a Machine Learning Model
  • Closing Remarks
  • Further Reading
• 10. Exploring Semantic Relationships with Word Embeddings
  • What Youll Learn and What Well Build
  • The Case for Semantic Embeddings
    • Word Embeddings
    • Analogy Reasoning with Word Embeddings
    • Types of Embeddings
      • Word2Vec
      • GloVe
      • FastText
      • Deep contextualized embeddings
  • Blueprint: Using Similarity Queries on Pretrained Models
    • Loading a Pretrained Model
    • Similarity Queries
  • Blueprints for Training and Evaluating Your Own Embeddings
    • Data Preparation
      • Phrases
    • Blueprint: Training Models with Gensim
    • Blueprint: Evaluating Different Models
      • Looking for similar concepts
      • Analogy reasoning on our own models
  • Blueprints for Visualizing Embeddings
    • Blueprint: Applying Dimensionality Reduction
    • Blueprint: Using the TensorFlow Embedding Projector
    • Blueprint: Constructing a Similarity Tree
  • Closing Remarks
  • Further Reading
• 11. Performing Sentiment Analysis on Text Data
  • What Youll Learn and What Well Build
  • Sentiment Analysis
  • Introducing the Amazon Customer Reviews Dataset
  • Blueprint: Performing Sentiment Analysis Using Lexicon-Based Approaches
    • Bing Liu Lexicon
    • Disadvantages of a Lexicon-Based Approach
  • Supervised Learning Approaches
    • Preparing Data for a Supervised Learning Approach
  • Blueprint: Vectorizing Text Data and Applying a Supervised Machine Learning Algorithm
    • Step 1: Data Preparation
    • Step 2: Train-Test Split
    • Step 3: Text Vectorization
    • Step 4: Training the Machine Learning Model
  • Pretrained Language Models Using Deep Learning
    • Deep Learning and Transfer Learning
  • Blueprint: Using the Transfer Learning Technique and a Pretrained Language Model
    • Step 1: Loading Models and Tokenization
    • Step 2: Model Training
    • Step 3: Model Evaluation
  • Closing Remarks
  • Further Reading
• 12. Building a Knowledge Graph
  • What Youll Learn and What Well Build
  • Knowledge Graphs
    • Information Extraction
  • Introducing the Dataset
  • Named-Entity Recognition
    • Blueprint: Using Rule-Based Named-Entity Recognition
    • Blueprint: Normalizing Named Entities
    • Merging Entity Tokens
  • Coreference Resolution
    • Blueprint: Using spaCys Token Extensions
    • Blueprint: Performing Alias Resolution
    • Blueprint: Resolving Name Variations
    • Blueprint: Performing Anaphora Resolution with NeuralCoref
    • Name Normalization
    • Entity Linking
  • Blueprint: Creating a Co-Occurrence Graph
    • Extracting Co-Occurrences from a Document
    • Visualizing the Graph with Gephi
  • Relation Extraction
    • Blueprint: Extracting Relations Using Phrase Matching
    • Blueprint: Extracting Relations Using Dependency Trees
  • Creating the Knowledge Graph
    • Dont Blindly Trust the Results
  • Closing Remarks
  • Further Reading
• 13. Using Text Analytics in Production
  • What Youll Learn and What Well Build
  • Blueprint: Using Conda to Create Reproducible Python Environments
  • Blueprint: Using Containers to Create Reproducible Environments
  • Blueprint: Creating a REST API for Your Text Analytics Model
  • Blueprint: Deploying and Scaling Your API Using a Cloud Provider
  • Blueprint: Automatically Versioning and Deploying Builds
  • Closing Remarks
  • Further Reading
• Index

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