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Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data, by Dipanjan Sarkar
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Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.
Text Analytics with Python�teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem.�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.
What You Will Learn:
- Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure
- Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews
- Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern
Who This Book Is For :
IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data
- Sales Rank: #125040 in Books
- Published on: 2016-12-01
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .84" w x 6.14" l,
- Binding: Paperback
- 385 pages
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
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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Excellent book. I'm new to text mining and felt ...
By quant
Excellent book. I'm new to text mining and felt some of the chapters got me up to speed immediately
2 of 2 people found the following review helpful.
A very useful and handy book for NLP!
By Amazon Customer
This is a noteworthy book for beginners who wants to learn Natural Language Processing using Python software stack. This book is well written and in a very structured form maintaining the flow. It is definitely worth a read for someone who is overwhelmed by ample resources about NLP on the internet and is wondering for a headstart for the same. This book is not only structured but also has all the NLP concepts well explained accompanied by practical guide.
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!
2 of 2 people found the following review helpful.
Structure and content is impressive!
By Amazon Customer
What strikes me about this book is its amazing content structure! For someone who is new to playing with text or someone who has to get their hands dirty with unstructured text, this is the best buy! As someone new to log analytics, I picked this book and read the concepts right from the basics! Personally not only was it a great start to read and understand things but good for the curiosity it infused in me towards the work that I was doing! I did recommend it to colleagues at work and it helped them too! The code snippets are beautifully explained and for someone looking for hands-on start to the topic, exploring the git link could really help! Good job with the book!
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