Blueprints For Text Analytics Using Python : Machine Learning Based Solutions For Common Real World

  Turning text into valuable information is essential for businesses looking to gain a competitive advantage. There have been many improvements in natural language processing, giving users a lot of options when choosing how to approach a problem. However, it’s not always clear which NLP tools or libraries would work best for a specific business use case–or which techniques you should use and in what order. This practical book provides theoretical background and real-world case studies with detailed code examples to help developers and data scientists obtain insight from online text. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler use blueprints for text-related problems that apply state-of-the-art machine learning methods in Python. If you have a fundamental understanding of statistics and machine learning along with basic programming experience in Python, you’re ready to get started. You’ll learn how to: Crawl and clean, then explore and visualize textual data in different formats
Preprocess and vectorize text for machine learning
Apply methods for classification, topic analysis, summarization, and knowledge extraction
Use semantic word embeddings and deep learning approaches for complex problems
Work with Python NLP libraries like spaCy, NLTK, and Gensim in combination with scikit-learn, Pandas, and PyTorch

Ideal for readers interested in reading.