Text Analytics with Python A Practical Real-World Approach to Gaining Actionable Insights from your Data
Text Analytics with Python by Dipanjan Sarkar, published by Apress on December 1, 2016, is a comprehensive guide designed to help readers derive actionable insights from data using Python. This 1st edition, comprising 385 pages, covers both basic and advanced concepts in text analytics and natural language processing. The book focuses on essential algorithms and techniques, including text classification, clustering, topic modeling, and text summarization, providing a solid foundation for understanding how to apply these methods effectively.
Readers will find detailed explanations of major concepts in natural language processing and text analytics, along with practical applications. The book emphasizes the importance of selecting the appropriate technique for specific problems and offers insights into the mathematical concepts behind each method. With a focus on implementing Python and popular open-source libraries such as NLTK, Gensim, and spaCy, this resource is suitable for IT professionals, analysts, developers, and data scientists interested in leveraging textual data for analysis and insight generation.
Official synopsis Publisher
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization.
Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. 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.
What You Will Learn:
- Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure
- Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews
- Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern
Who This Book Is For :
IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data
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