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

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.