Update README.md

Author: dipanjanS

README.md

@@ -1,10 +1,10 @@
-# Text Analytics with Python
-### A Practical Real-World Approach to Gaining Actionable Insights from your Data
+# 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 385 pages of useful information 
+"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. 
@@ -33,26 +33,14 @@ Keep watching this space!
 <br><br>
 
 ## About the book 
-<a target="_blank" href="https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X/ref=sr_1_1?ie=UTF8&qid=1481143141&sr=8-1&keywords=text+analytics+with+python">
-  <img src="./media/banners/cover_front.png" alt="Book Cover" width="250" align="left"/>
+<a target="_blank" href="https://www.amazon.com/Text-Analytics-Python-Practitioners-Processing/dp/1484243536/ref=sr_1_1?dchild=1&keywords=Text+Analytics+with+Python&qid=1599726217&sr=8-1">
+  <img src="https://i.imgur.com/aLk4L4h.png" alt="Book Cover" width="270" align="left"/>
 </a>
 
-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
+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. 
 
-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.
+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.
 
 <div style='font-size:0.5em;'>
 <sup>Edition: 1st<br>