Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data - Tapa blanda

Acerca del autor

Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company which is on a mission to make the world more connected and productive. He primarily works on Analytics, Business Intelligence, Application Development and building large scale Intelligent Systems. He received his master's degree in Information Technology from the International Institute of Information Technology, Bangalore with a focus on Data Science and Software Engineering. He is also an avid supporter of self-learning, especially Massive Open Online Courses and holds a Data Science Specialization from Johns Hopkins University on Coursera.

He has been an analytics practitioner for over 4 years now specializing in statistical, predictive and text analytics. He has also authored a couple of books on R and Machine Learning and occasionally reviews technical books and acts as a course beta tester for Coursera. Dipanjan's interests include learning about new technology, financial markets, disruptive start-ups, data science and more recently, artificial intelligence and deep learning. In his spare time he loves reading, gaming and watching popular sitcoms and football.

De la contraportada

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. 

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.

This book:

• Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics
• Includes practical real-world examples of techniques for implementation, such as building a text classification system to categorize news articles, analyzing app or game reviews using topic modeling and text summarization, and clustering popular movie synopses and analyzing the sentiment of movie reviews
• Shows implementations based on Python and several popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern