Blueprints for Text Analytics Using Python:Machine Learning-Based Solutions for Common Real World (NLP) Applications


Blueprints for Text Analytics Using Python:Machine Learning-Based Solutions for Common Real World (NLP) Applications 1st Edition
by:Jens Albrecht , Sidharth Ramachandran , Christian Winkler
pages 页数:424 pages
ISBN-10 书号:149207408X
ISBN-13 书号:9781492074083
Dimensions:7 x 0.86 x 9.19 inches
Publisher Finelybook 出版社:O’Reilly Media; 1st edition (January 5, 2021)
Language 语言:English

Book Description
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it’s not always clear which NLP tools or libraries would work for a business’s needs, or which techniques you should use and in what order.
This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.

Extract data from APIs and web pages
Prepare textual data for statistical analysis and machine learning
Use machine learning for classification, topic modeling, and summarization
Explain AI models and classification results
Explore and visualize semantic similarities with word embeddings
Identify customer sentiment in product reviews
Create a knowledge graph based on named entities and their relations
Table of contents
Preface
Chapter 1. Gaining Early Insights from Textual Data
Chapter 2. Extracting Textual Insights with APls
Chapter 3. Scraping Websites and Extracting Data
Chapter 4. Preparing Textual Data for Statistics and Machine Learning
Chapter 5 Feature Engineering and Syntactic Similarity
Chapter 6. Text Classification Algorithms
Chapter 7. How to Explain a Text Classifier
Chapter 8. Unsupervised Methods: Topic Modeling and Clustering
Chapter 9. Text Summarization
Chapter 10. Exploring Semantic Relationships with Word embeddings
Chapter 11. Performing Sentiment Analysis on Text Data
Chapter 12. Building a Knowledge graph
Chapter 13. Using Text Analytics in production
Index
About the authors
Colophon

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