Applied text analysis with Python: enabling language-aware data products with machine learning
Bengfort, Benjamin
Applied text analysis with Python: enabling language-aware data products with machine learning - Mumbai O'Reilly Media 2021 - xvii, 310 p.
All Indian Reprints of O'Reilly are printed in Grayscale.
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
9789352137435
Python (Computer program language)
Natural language processing (Computer science)
Machine learning
005.133 / BEN
Applied text analysis with Python: enabling language-aware data products with machine learning - Mumbai O'Reilly Media 2021 - xvii, 310 p.
All Indian Reprints of O'Reilly are printed in Grayscale.
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
9789352137435
Python (Computer program language)
Natural language processing (Computer science)
Machine learning
005.133 / BEN