Free Download Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

Free Download Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

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Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

Free Download Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data

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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. This book: 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

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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.

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Product details

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:

4.2 out of 5 stars

26 customer reviews

Amazon Best Sellers Rank:

#202,847 in Books (See Top 100 in Books)

One of my go-to books for natural language processing with Python has been Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. This has been the book for me and was one of my dissertation references. I used this book so much, that I I had to buy a second copy of this book because I wore the first one out. I’ve read many other NLP books but haven’t found any that could match this book – till now.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.

The author goes through a number of very useful topics, and having already been trained in NLP and semantic analysis this was a nice overview with detailed code. I have not tested all the classes and functions but the flow of the book seems right for a crash course in what to do with million reviews in one week!

Nice, concise discussion of several hot topics of Text Analytics. I recommend for new beginners.

Good examples with some tools not included in other cookbooks ( like space)

Great into chapter on linguistics and code that you can download and be up and running immediately. Well written as well. For me, it's a fascinating topic, and properly addressed in this text.

Excellent book. I'm new to text mining and felt some of the chapters got me up to speed immediately

First half of book is terminology and how language works. Not enough focus on implementing projects

Very comprehensive coverage of almost all aspects of traditional Natural Language Processing (NLP), contains tons of code using pure Python as well as using NLTK, pattern, gensim and SpaCy libraries that can be extended or reused for your own projects. As another reviewer has noted, this is an excellent replacement (slightly less deep but much broader coverage) for Bird, et al's Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. If I had a complaint, it is that the book tends to go a little heavy on theory, since its stated target audience is NLP practitioners. One other thing is that there is zero coverage of Deep Learning, although that wasn't something I was looking for when reading this book. Overall, I thought that this was an excellent and broad introduction to traditional NLP. It will get you started with reusable code snippets, and will give you (more than) enough theoretical understanding so you can consult more advanced books or papers as needed.

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