Title
Text Analytics With Python: A Practitioner'S Guide To Natural Language Processing
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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.Youll See How To Use The Latest Stateoftheart 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 Learningbased 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 Realworld Example Of Movie Recommenders, Along With Sentiment Analysis Using Supervised And Unsupervised Techniques.There Is Also A Chapter Dedicated To Semantic Analysis Where Youll 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.What You'Ll Learn Understand Nlp And Text Syntax, Semantics And Structure Discover Text Cleaning And Feature Engineering Review Text Classification And Text Clustering Assess Text Summarization And Topic Models Study Deep Learning For Nlpwho This Book Is Forit Professionals, Data Analysts, Developers, Linguistic Experts, Data Scientists And Engineers And Basically Anyone With A Keen Interest In Linguistics, Analytics And Generating Insights From Textual Data.
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- Q: How many pages are in this book? A: This book contains six hundred ninety-eight pages. It provides extensive coverage on Natural Language Processing and text analytics.
- Q: What is the binding type of this book? A: The binding type is paperback. This format is lightweight and flexible, making it easy to carry and read.
- Q: What are the dimensions of the book? A: The book measures seven point zero one inches in length, one point five eight inches in width, and ten inches in height. These dimensions make it a standard-sized paperback.
- Q: Who is the author of this book? A: The author is Dipanjan Sarkar. He specializes in Natural Language Processing and has extensive experience in the field.
- Q: What category does this book fall under? A: This book falls under the category of Intelligence & Semantics. It focuses on advanced topics in text analytics.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It covers foundational concepts of Natural Language Processing before advancing to more complex topics.
- Q: Can I use this book for academic purposes? A: Yes, this book is suitable for academic purposes. It is a comprehensive guide for students and professionals in data science and analytics.
- Q: Does this book include practical examples? A: Yes, the book includes practical examples and case studies. It helps readers apply theoretical knowledge in real-world scenarios.
- Q: Is this book relevant for current NLP trends? A: Yes, the book addresses current trends in Natural Language Processing. It has been updated to reflect recent advancements and techniques.
- Q: How do I take care of this paperback book? A: To care for this paperback book, store it in a cool, dry place and avoid exposure to direct sunlight. This will help preserve its condition.
- Q: Is this book safe for children? A: No, this book is not specifically designed for children. It targets professionals and students with a background in data analytics.
- Q: What if my book arrives damaged? A: If your book arrives damaged, contact the seller for a return or exchange. Most sellers have a customer service policy to address such issues.
- Q: Can I find this book in digital format? A: Yes, this book may be available in digital format. Check online retailers for eBook options.
- Q: Is there a chapter on named entity recognition? A: Yes, there is a dedicated chapter on named entity recognition. It provides guidance on building a NER system from scratch.
- Q: Does the book cover sentiment analysis techniques? A: Yes, the book covers both supervised and unsupervised sentiment analysis techniques. This includes practical applications and examples.
- Q: Are there any prerequisites for reading this book? A: Basic knowledge of Python programming is recommended. Familiarity with text data and analytics concepts will also be beneficial.