- Paperback: 385 pages
- Publisher: Apress; 1st ed. edition (December 1, 2016)
- Language: English
- ISBN-10: 148422387X
- ISBN-13: 978-1484223871
- Product Dimensions: 6.1 x 0.9 x 9.2 inches
- Shipping Weight: 1.5 pounds (View shipping rates and policies)
- Average Customer Review: 24 customer reviews
- Amazon Best Sellers Rank: #243,010 in Books (See Top 100 in Books)
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Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data 1st ed. Edition
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From the Back Cover
Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem.
Text Analytics with Python teaches you both basic and advanced concepts, including text and language syntax, structure, semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.
- Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics
- Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews
- Shows implementations 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 and Pattern
About the Author
Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company which is on a mission to make the world more connected and productive. He primarily works on analytics, business intelligence, application development and building large scale intelligent systems. He received his master's degree in Information Technology from the International Institute of Information Technology, Bangalore with a focus on data science and software engineering. He has been an analytics practitioner for over 4 years now specializing in statistical, predictive and text analytics. He has also authored a book on Machine Learning with R and occasionally reviews technical books. Dipanjan's interests include learning about new technology, disruptive start-ups and data science. In his spare time he loves reading, gaming and watching popular sitcoms and football.
Top customer reviews
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This is a great book and has now taken a permanent place on my bookshelf.
Unlike many books that I run across, this book spends plenty of time talking about the theory behind things rather than just doing some hand-waving and then showing some code. In fact, there isn’t any code (that I saw) until page 41. That’s impressive these days. Here’s a quick overview of the book’s layout:
Chapter 1 provides the baseline for Natural Language. This is a very good overview for anyone that’s never worked much with NLP.
Chapter 2 is a python ‘refresher’. If you don’t know python at all but know some other language, this should get you started enough to use the rest of the book.
Chapter’s 3 – 7 is there the real fun begins. These chapters cover Text Classification, Summarization Similarity / Clustering and Semantic / Sentiment Analysis.
If you have some familiarity with python and NLP, you can jump to Chapter 3 and dive into the details.
What I really like about this book is that it places theory first. I’m a big fan of ‘learning by doing’ but I think before you can ‘do’ you need to know ‘why’ you are doing what you are doing. The code in the book is really well done as well and uses the NLTK, Sklearn and gensim libraries for most of the work. Additionally, there are multiple ‘build your own’ sections where the author provides a very good overview (and walk-through) of what it takes to build your own functionality for your own NLP work.
This book is also helpful for programmers who is willing to get into NLP. This book covers a wide range of topic including text summarization, text clustering, text classification, semantic and sentiment analysis in depth with good amount of practical exposure.
Anyone can be a pro in NLP after reading this book and will have a clear idea to apply right NLP concepts at the right place in real world scenarios!
I highly recommend this book to anyone willing to learn NLP especially to beginners and intermediate programmers!
Most recent customer reviews
I've battled my way through the book and was about to give up on several occasions.Read more
I, for one, would like a good book on NLTP based solely on Python 3.Read more