Applied Text Analysis with Python

Natural language is a rich and underutilized source of data, and this book provides a data scientists approach to building language-aware products with applied machine learning. It covers preprocess and vectorize text, perform document classification and topic modeling, steer the model selection process, extract key phrases, named entities, and graph structures, build a dialog framework, and use Spark to scale processing power and neural networks.

Format: Paperback / softback
Length: 350 pages
Publication date: 31 July 2018
Publisher: O'Reilly Media, Inc, USA

Natural language is a rich and underutilized source of data, constantly changing and adapting in context. It contains information that is not conveyed by traditional data sources. This practical book presents a data scientists approach to building language-aware products with applied machine learning. It covers robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, readers will be equipped with practical methods to solve complex real-world problems.

Preprocess and vectorize text into high-dimensional feature representations.

Perform document classification and topic modeling.

Steer the model selection process with visual diagnostics.

Extract key phrases, named entities, and graph structures to reason about data in text.

Build a dialog framework to enable chatbots and language-driven interaction.

Use Spark to scale processing power and neural networks to scale model complexity.

Weight: 584g
Dimension: 217 x 233 x 17 (mm)
ISBN-13: 9781491963043