Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.
Key Features
Discover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and Keras
Hands-on text analysis with Python, featuring natural language processing and computational linguistics algorithms
Learn deep learning techniques for text analysis
Book Description
Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.
This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.
You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.
This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
What you will learn
Why text analysis is important in our modern age
Understand NLP terminology and get to know the Python tools and datasets
Learn how to pre-process and clean textual data
Convert textual data into vector space representations
Using spaCy to process text
Train your own NLP models for computational linguistics
Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn
Employ deep learning techniques for text analysis using Keras
Who This Book Is For
This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Table of Contents
What is Text Analysis?
Python Tips for Text Analysis
spaCy’s Language Models
Gensim – Vectorizing text and transformations and n-grams
POS-Tagging and its Applications
NER-Tagging and its Applications
Dependency Parsing
Top Models
Advanced Topic Modelling
Clustering and Classifying Text
Similarity Queries and Summarization
Word2Vec, Doc2Vec and Gensim
Deep Learning for text
Keras and spaCy for Deep Learning
Sentiment Analysis and ChatBots
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About the Author
Bhargav Srivinasa-Desikan is a student researcher working for INRIA in Lille, France. He is part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation and data visualization. He also contributes to open source machine learning projects, particularly dynamic topic models for Gensim.
Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.
Key Features
Book Description
Modern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.
This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.
You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.
This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.
What you will learn
Who This Book Is For
This book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!
Table of Contents
**
About the Author
Bhargav Srivinasa-Desikan is a student researcher working for INRIA in Lille, France. He is part of the MODAL (Models of Data Analysis and Learning) team, and he works on metric learning, predictor aggregation and data visualization. He also contributes to open source machine learning projects, particularly dynamic topic models for Gensim.