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Text Analytics with Python de Dipanjan Sarkar

Text Analytics with Python

Autor Dipanjan Sarkar
en Limba Engleză Paperback – 22 mai 2019

Metodologia propusă în Text Analytics with Python se bazează pe o arhitectură riguroasă care transformă datele textuale brute în perspective strategice. Observăm o abordare modulară, unde fluxul de lucru începe cu designul mediului de lucru în Python 3.x și avansează sistematic prin etapele critice de curățare a textului, ingineria caracteristicilor și implementarea modelelor de învățare automată. Suntem de părere că forța acestui volum rezidă în echilibrul dintre modelele statistice tradiționale și noile metode bazate pe deep learning embedding, oferind cititorului instrumentele necesare pentru a naviga între algoritmi clasici și arhitecturi neuronale complexe.

Structura celor 674 de pagini indică o progresie logică: primele capitole consolidează fundamentele procesării textului, în timp ce secțiunile mediane se concentrează pe clasificare și clustering. Capitolul dedicat analizei semantice este deosebit de valoros, permițând implementarea unui sistem de recunoaștere a entităților numite (NER) fără a depinde exclusiv de biblioteci pre-antrenate. Complementar volumului Natural Language Processing Recipes, care se axează pe soluții punctuale pentru proiecte end-to-end, lucrarea lui Dipanjan Sarkar oferă o profunzime teoretică și o capacitate de personalizare a modelelor pe care rețetarele tehnice adesea o omit.

Poziționată ca o extensie naturală a lucrărilor sale anterioare, cum ar fi Practical Machine Learning with Python, această ediție a doua rafinează conceptele de transfer learning și deep learning aplicate specific pe text. Subliniem faptul că autorul nu se limitează la prezentarea codului, ci insistă pe interpretarea modelelor de topicuri și pe tehnici de sumarizare, folosind seturi de date relevante, precum conferințele NIPS, pentru a ancora teoria în realitatea analizei de date contemporane.

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Specificații

ISBN-13: 9781484243534
ISBN-10: 1484243536
Pagini: 674
Ilustrații: XXIV, 674 p. 189 illus.
Dimensiuni: 178 x 254 x 37 mm
Greutate: 1.29 kg
Ediția:2nd edition
Editura: Apress
Locul publicării:Berkeley, CA, United States

De ce să citești această carte

Recomandăm această carte profesioniștilor IT și cercetătorilor de date care doresc să stăpânească procesarea limbajului natural dincolo de utilizarea simplă a unor API-uri. Veți câștiga abilitatea de a construi sisteme robuste de analiză a sentimentelor și sumarizare automată, beneficiind de un fundament solid în Python 3. Este resursa ideală pentru a înțelege mecanismele interne ale algoritmilor de text analytics și pentru a implementa soluții scalabile de tip enterprise.

Despre autor

Dipanjan Sarkar este un expert recunoscut în domeniul științei datelor și ingineriei software, cu o specializare pronunțată în învățare automată și procesarea limbajului natural. Autor prolific, acesta a publicat lucrări de referință precum Practical Machine Learning with Python și Hands-On Transfer Learning with Python, demonstrând o capacitate remarcabilă de a simplifica concepte complexe de deep learning pentru practicieni. Experiența sa acoperă atât ecosistemul Python, cât și cel bazat pe R, fiind un promotor al analizelor de date bazate pe dovezi și al implementărilor tehnice riguroase în mediul academic și industrial.

Descriere scurtă

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. 
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.

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 NLP

Who This Book Is For

IT 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.

Cuprins

Chapter 1:  Natural Language Processing Basics.- Chapter 2:  Python for Natural Language Processing.- Chapter 3:  Processing and Understanding Text.- Chapter 4:  Feature Engineering for Text Data.- Chapter 5: Text Classification.- Chapter 6: Text summarization and topic modeling.- Chapter 7: Text Clustering and Similarity analysis.- Chapter 8: Sentiment Analysis.- Chapter 9: Deep learning in NLP.

Notă biografică

Dipanjan (DJ) Sarkar is a Data Scientist at Red Hat, a published author and a consultant and trainer. He has consulted and worked with several startups as well as Fortune 500 companies like Intel. He primarily works on leveraging data science, advanced analytics, machine learning and deep learning to build large- scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning and massive open online courses. He has recently ventured into the world of open-source products to improve the productivity of developers across the world.


Dipanjan has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning. Having a passion for data science and education, he also acts as an AI Consultant and Mentor at various organizations like Springboard, where he helps people build their skills on areas like Data Science and Machine Learning. He also acts as a key contributor and Editor for Towards Data Science, a leading online journal focusing on Artificial Intelligence and Data Science. Dipanjan has also authored several books on R, Python, Machine Learning, Social Media Analytics, Natural Language Processing and Deep Learning.


Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science, artificial intelligence and deep learning. In his spare time he loves reading, gaming, watching popular sitcoms and football and writing interesting articles on https://medium.com/@dipanzan.sarkar and https://www.linkedin.com/in/dipanzan. He is also a strong supporter of open-source and publishes his code and analyses from his books and articles on GitHub at https://github.com/dipanjanS.


Textul de pe ultima copertă

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python.

This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods.

Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning.

While the overall structure of the book remainsthe same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release.
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Also the key selling points
• Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP 
• Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP
• Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis