Blueprints for Text Analytics Using Python by Jens Albrecht -- Read -- Imperial Library of Trantor

Index

Preface

Approach of the Book Prerequisites Some Important Libraries to Know Books to Read Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments

1. Gaining Early Insights from Textual Data

What You’ll Learn and What We’ll 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 You’ll Learn and What We’ll 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 User’s Timeline Extracting Data from the Streaming API

Closing Remarks

3. Scraping Websites and Extracting Data

What You’ll Learn and What We’ll 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 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 You’ll Learn and What We’ll 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 You’ll Learn and What We’ll Build A Toy Dataset for Experimentation Blueprint: Building Your Own Vectorizer

Enumerating the Vocabulary Vectorizing Documents The Document-Term Matrix The Similarity Matrix

Bag-of-Words Models

Blueprint: Using scikit-learn’s CountVectorizer Blueprint: Calculating Similarities

TF-IDF Models

Optimized Document Vectors with TfidfTransformer Introducing the ABC Dataset Blueprint: Reducing Feature Dimensions Blueprint: Improving Features by Making Them More Specific Blueprint: Using Lemmas Instead of Words for Vectorizing Documents Blueprint: Limit Word Types Blueprint: Remove Most Common Words Blueprint: Adding Context via N-Grams

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 You’ll Learn and What We’ll 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

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 You’ll Learn and What We’ll 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 You’ll Learn and What We’ll 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 You’ll Learn and What We’ll 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 You’ll Learn and What We’ll Build The Case for Semantic Embeddings

Word Embeddings Analogy Reasoning with Word Embeddings Types of Embeddings

Blueprint: Using Similarity Queries on Pretrained Models

Loading a Pretrained Model Similarity Queries

Blueprints for Training and Evaluating Your Own Embeddings

Data Preparation Blueprint: Training Models with Gensim Blueprint: Evaluating Different 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 You’ll Learn and What We’ll 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 You’ll Learn and What We’ll 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 spaCy’s 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

Don’t Blindly Trust the Results

Closing Remarks Further Reading

13. Using Text Analytics in Production

What You’ll Learn and What We’ll 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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