Blueprints for Text Analytics Using Python

Blueprints for Text Analytics Using Python
  • eBook:
    Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications
  • Author:
    Jens Albrecht, Sidharth Ramachandran, Christian Winkler
  • Edition:
    1 edition
  • Categories:
  • Data:
    January 5, 2021
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    424 pages
  • Format:

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


1. Gaining Early Insights from Textual Data
2. Extracting Textual Insights with APIs
3. Scraping Websites and Extracting Data
4. Preparing Textual Data for Statistics and Machine Learning
5. Feature Engineering and Syntactic Similarity
6. Text Classification Algorithms
7. How to Explain a Text Classifier
8. Unsupervised Methods: Topic Modeling and Clustering
9. Text Summarization
10. Exploring Semantic Relationships with Word Embeddings
11. Performing Sentiment Analysis on Text Data
12. Building a Knowledge Graph
13. Using Text Analytics in Production

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