Summary and Info
Build cool NLP and machine learning applications using NLTK and other Python libraries About This BookExtract information from unstructured data using NLTK to solve NLP problemsAnalyse linguistic structures in text and learn the concept of semantic analysis and parsingLearn text analysis, text mining, and web crawling in a simplified mannerWho This Book Is ForIf you are an NLP or machine learning enthusiast with some or no experience in text processing, then this book is for you. This book is also ideal for expert Python programmers who want to learn NLTK quickly.What You Will LearnGet a glimpse of the complexity of natural languages and how they are processed by machinesClean and wrangle text using tokenization and chunking to help you better process dataExplore the different types of tags available and learn how to tag sentencesCreate a customized parser and tokenizer to suit your needsBuild a real-life application with features such as spell correction, search, machine translation and a question answering systemRetrieve any data content using crawling and scrapingPerform feature extraction and selection, and build a classification system on different pieces of textsUse various other Python libraries such as pandas, scikit-learn, matplotlib, and gensimAnalyse social media sites to discover trending topics and perform sentiment analysisIn DetailNatural Language Processing (NLP) is the field of artificial intelligence and computational linguistics that deals with the interactions between computers and human languages. With the instances of human-computer interaction increasing, it's becoming imperative for computers to comprehend all major natural languages. Natural Language Toolkit (NLTK) is one such powerful and robust tool.You start with an introduction to get the gist of how to build systems around NLP. We then move on to explore data science-related tasks, following which you will learn how to create a customized tokenizer and parser from scratch. Throughout, we delve into the essential concepts of NLP while gaining practical insights into various open source tools and libraries available in Python for NLP. You will then learn how to analyze social media sites to discover trending topics and perform sentiment analysis. Finally, you will see tools which will help you deal with large scale text.By the end of this book, you will be confident about NLP and data science concepts and know how to apply them in your day-to-day work.
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