Text Analytics with Python, 2nd EditionJune 5, 2021
Book Name: Text Analytics with Python, 2nd Edition
Author: Dipanjan Sarkar
File size: 35.3 MB
File format: PDF, ePub
Text Analytics with Python, 2nd Edition Book Description:
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 major modifications and new themes based on the recent tendencies in NLP.
You will see how to use the most recent state-of-the-art frameworks in NLP, coupled with machine learning and profound learning models such as supervised opinion analysis powered by Python to fix actual case studies. Begin by reviewing Python for NLP fundamentals on strings and text data and move to technology representation methods for text information, including both traditional statistical models and newer deep learning-based embedding versions. Improved methods and new techniques round parsing and processing are discussed as well.
Text summarization and subject models have been overhauled so the book showcases the way to build, tune, and translate topic models in the context of a fascination dataset on NIPS conference papers. Additionally, the publication covers text similarity techniques using a real-world instance of movie recommenders, together with opinion analysis using supervised and unsupervised methods.
While the general structure of the book remains the same, the whole code base, modules, and chapters was 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
- Overview text classification and text clustering
- Assess text summarization and subject models
- Research deep learning for NLP
Whos is the book for:
IT professionals, information analysts, programmers, linguistic experts, data engineers and scientists and essentially anyone with a keen interest in linguistics, analytics and creating insights from textual data.