Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust
environment for performing text analytics. This second edition has gone through a major revamp
and introduces several significant changes and new topics based on the recent trends in NLP.
You'll see how to use the latest state-of-the-art frameworks in NLP coupled with machine
learning and deep learning models for supervised sentiment analysis powered by Python to solve
actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data
and move on to engineering representation methods for text data including both traditional
statistical models and newer deep learning-based embedding models. Improved techniques and new
methods around parsing and processing text are discussed as well. Text summarization and topic
models have been overhauled so the book showcases how to build tune and interpret topic
models in the context of an interest dataset on NIPS conference papers. Additionally the book
covers text similarity techniques with a real-world example of movie recommenders along with
sentiment analysis using supervised and unsupervised techniques. There is also a chapter
dedicated to semantic analysis where you'll see how to build your own named entity recognition
(NER) system from scratch. While the overall structure of the book remains the same the entire
code base modules and chapters has been updated to the latest Python 3.x release. What You'll
Learn -Understand NLP and text syntax semantics and structure-Discover text cleaning and
feature engineering-Review text classification and text clustering - Assess text summarization
and topic models- Study deep learning for NLP Who This Book Is For IT professionals data
analysts developers linguistic experts data scientists and engineers and basically anyone
with a keen interest in linguistics analytics and generating insights from textual data.
