Applied text analysis with Python : enabling language-aware data products with machine learning /

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 source...

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Bibliographic Details
Main Authors: Bengfort, Benjamin, 1984- (Author), Bilbro, Rebecca (Author), Ojeda, Tony (Author)
Format: Printed Book
Language:English
Edition:First edition.
Subjects:
Natural language processing (Computer science)
Python (Computer program language)
Machine learning.
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245 1 0 |a Applied text analysis with Python :  |b enabling language-aware data products with machine learning /  |c Benjamin Bengfort, Rebecca Bilbro, and Tony Ojeda. 
250 |a First edition. 
300 |a xviii, 310 pages :  |b illustrations ;  |c 25 cm 
500 |a Includes index. 
505 0 0 |t Language and computation --  |t Building a custom corpus --  |t Corpus preprocessing and wrangling --  |t Text vectorization and transformation pipelines --  |t Classification for text analysis --  |t Clustering for text similarity --  |t Context-aware text analysis --  |t Text visualization --  |t Graph analysis of text --  |t Chatbots --  |t Scaling text analytics with multiprocessing and Spark --  |t Deep learning and beyond. 
520 |a 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 will 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.-- 
650 0 |a Natural language processing (Computer science) 
650 0 |a Python (Computer program language) 
650 0 |a Machine learning. 
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650 7 |a Natural language processing (Computer science)  |2 fast 
650 7 |a Python (Computer program language)  |2 fast 
700 1 |a Bilbro, Rebecca,  |e author. 
700 1 |a Ojeda, Tony,  |e author. 
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