Blueprints for Text Analytics using Python
Machine Learning Based Solutions for Common Real World (NLP) Applications
Published on 22. December 2020
Book
Paperback/Softback
350 pages
978-1-4920-7408-3 (ISBN)
Description
Turning text into valuable information is essential for businesses looking to gain a competitive advantage. With recent improvements in natural language processing (NLP), users now have many options for solving complex challenges. But it's not always clear which NLP tools or libraries would work for a business's needs, or which techniques you should use and in what order.
This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
Extract data from APIs and web pages
Prepare textual data for statistical analysis and machine learning
Use machine learning for classification, topic modeling, and summarization
Explain AI models and classification results
Explore and visualize semantic similarities with word embeddings
Identify customer sentiment in product reviews
Create a knowledge graph based on named entities and their relations
This practical book provides data scientists and developers with blueprints for best practice solutions to common tasks in text analytics and natural language processing. Authors Jens Albrecht, Sidharth Ramachandran, and Christian Winkler provide real-world case studies and detailed code examples in Python to help you get started quickly.
Extract data from APIs and web pages
Prepare textual data for statistical analysis and machine learning
Use machine learning for classification, topic modeling, and summarization
Explain AI models and classification results
Explore and visualize semantic similarities with word embeddings
Identify customer sentiment in product reviews
Create a knowledge graph based on named entities and their relations
More details
Language
English
Place of publication
Sebastopol
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 227 mm
Width: 179 mm
Thickness: 25 mm
Weight
744 gr
ISBN-13
978-1-4920-7408-3 (9781492074083)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Jens Albrecht
Blueprints for Text Analytics Using Python
E-Book
12/2020
O'Reilly
€58.99
Available for download
Jens Albrecht | Sidharth Ramachandran | Christian Winkler
Blueprints for Text Analytics Using Python
E-Book
12/2020
O'Reilly
€58.99
Available for download
Persons
Jens Albrecht is a full-time professor for Computer Science Department at the Nuremberg Institute of Technology. His work focuses on data management and analytics with a focus on text. He holds a doctorates degree in computer science. Before he rejoined academia in 2012, he has been working for over a decade in the industry as consultant and data architect. He is author of several articles on Big Data management and analysis. Sidharth Ramachandran currently leads a team of data scientists at GfK helping to build data products for the consumer goods industry. He has over 10 years of experience in software engineering and data science across telecom, banking and marketing industries. Sidharth also co-founded WACAO, a smart personal assistant on Whatsapp which was also featured on Techcrunch. He holds an undergraduate engineering degree from IIT Roorkee and an MBA from IIM Kozhikode. Sidharth is passionate about solving real problems through technology and loves to hack through personal projects in his free time. Christian Winkler is a Data Scientist and Machine Learning Architect. He holds a PhD in theoretical physics and has been working in the field of large data volumes and artificial intelligence for 20 years, with particular focus on scalable systems and intelligent algorithms for mass text processing. He is founder of datanizing GmbH, speaker at conferences and author of Machine Learning / Text Analytics articles.