Applied Text Analysis with Python

Enabling Language-Aware Data Products with Machine Learning

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, Data Processing
Cover of the book Applied Text Analysis with Python by Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda, O'Reilly Media
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Author: Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda ISBN: 9781491962992
Publisher: O'Reilly Media Publication: June 11, 2018
Imprint: O'Reilly Media Language: English
Author: Benjamin Bengfort, Rebecca Bilbro, Tony Ojeda
ISBN: 9781491962992
Publisher: O'Reilly Media
Publication: June 11, 2018
Imprint: O'Reilly Media
Language: English

From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning.

You’ll learn 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, you’ll be equipped with practical methods to solve any number of 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
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From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning.

You’ll learn 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, you’ll be equipped with practical methods to solve any number of complex real-world problems.

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