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Python Tips for Text Analysis

We mentioned in Chapter 1, What is Text Analysis, that we will be using Python throughout the book because it is an easy-to-use and powerful language. In this chapter, we will substantiate these claims, while also providing a revision course in basic Python for text analysis.

Why is this important? While we expect readers of the book to have a background in Python and high-school level math, it is still possible that it's been a while since you've written Python code – and even if you have, the Python code you write during text analysis and string manipulation is quite different from, say, building a website using the web framework Django. Following are the topics we will cover in this chapter:

Why Python?
Text manipulation in Python

Key benefits

• 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

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

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David Baron Aug 28, 2018

15 chapters of fast-paced learning - the author gives you the bootstrapped resources for success with NLP. Every concept is explained clearly and with powerful examples and analogies. Increasingly complex concepts are stacked upon each other to produce a comprehensive overview of everything in NLP, from linguistics-based methods to bag-of-words approaches, from k-means clustering and LDA models to the inevitable methods of deep learning. Highly recommended.

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TM Raghavan Aug 17, 2018

What I liked about this book is its style of delivery, an easy read one. As it states in the beginning, though fluency in Python or NLP concepts is better to make full use of the ideas, it still contains lots of code examples and analysis of their results, like any good book on this genre. The breadth and and the depth from the coverage of the book's focus are sufficient in my opinion.What I find missing in the book is a section on Glossary, encompassing statistics, vector /matrix algebra, NLP/IR, ANN (CNN/RNN), that are relevant to the readers' requirements. Perhaps this can be given in the beginning itself (like a Forward).Overall I find the book very useful for the students of final year Under-Graduate and Graduate programs besides researchers whose foci are outside of basic NLP but would want to analyse large texts for their research.

Juan Oct 08, 2018

Great book, up to date, engaging and covers a lot of topics clearly.

Victor Aug 27, 2018

Although the introduction states that is necessary be fluent in Python, this book is feasible for anyone who has just a basic understanding of Python or any other programming language. The book describes profoundly all the steps of a NLP analysis, setting comprehensible examples that allows the reader to understand every aspect of NLP. I would consider this book as an advanced and complete introduction, recommended to anyone who wants to begin and follow further on the topic but getting a solid base from this book. I would recommend it.

Itai Aug 14, 2018

Writing is a profession. Even excellent engineers may be bad writers, this is exactly what happens here.Super long sentences on trivial topics, and then just a taste from the really important stuff.For example: having several full pages on 1st grade stuff as "lower(), upper(), isNumeric()", having several pages on word2Vec King-man+woman = queen, and then have only a few lines about Doc2Vec which is the most important thing.

Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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Natural Language Processing and Computational Linguistics

Python Tips for Text Analysis

Why Python?

Text manipulation in Python

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Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

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