https://github.com/dipanjans/text-analytics-with-python

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.
https://github.com/dipanjans/text-analytics-with-python

clustering gensim natural-language natural-language-processing nltk pattern python scikit-learn semantic sentiment sentiment-analysis spacy stanford-nlp text-analytics text-classification text-summarization

Last synced: 4 days ago
JSON representation

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

• Host: GitHub
• URL: https://github.com/dipanjans/text-analytics-with-python
• Owner: dipanjanS
• License: apache-2.0
• Created: 2016-09-25T19:19:02.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-12-24T15:46:21.000Z (about 4 years ago)
• Last Synced: 2025-01-18T13:05:10.816Z (4 days ago)
• Topics: clustering, gensim, natural-language, natural-language-processing, nltk, pattern, python, scikit-learn, semantic, sentiment, sentiment-analysis, spacy, stanford-nlp, text-analytics, text-classification, text-summarization
• Language: Jupyter Notebook
• Homepage:
• Size: 38.8 MB
• Stars: 1,661
• Watchers: 118
• Forks: 845
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Text Analytics with Python - 2nd Edition

### A Practitioner's Guide to Natural Language Processing

Text analytics can be a bit overwhelming and frustrating at times

with the unstructured and noisy nature of textual data and the 

vast amount of information available. 

"Text Analytics with Python" is a book packed with 674 pages of useful information 

based on techniques, algorithms, experiences and various lessons learnt over time 

in analyzing text data. This repository contains datasets and code used in this book.

I will also be adding various notebooks and bonus content here from time to time. 

Keep watching this space!

## Get the book 





  





  





  





  





  












## About the book 



  



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.



Edition: 2nd


Pages: 674


Language: English


Book Title: Text Analytics with Python


Book Subtitle: A Practitioner's Guide to Natural Language Processing


Publisher: Apress (a part of Springer)
  

Print ISBN: 978-1-4842-4353-4
  

Online ISBN: 978-1-4842-4354-1


DOI: 10.1007/978-1-4842-4354-1


Copyright: Dipanjan Sarkar







With this book you will:

- Understanding NLP and text syntax, semantics and structure

- Discover text cleaning and feature engineering strategies

- Learn and implement text classification and text clustering

- Understand and build text summarization and topic models

- Learn about the promise of deep learning and transfer learning for NLP

- Implement hands-on examples based on Python and several popular open source libraries 

 in NLP and text analytics, such as the natural language toolkit (`nltk`), 

 `gensim`, `scikit-learn`, `spaCy`, `keras` and `tensorflow`