Data is the new oil and unstructured data—especially text, images, and videos—contains a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural language processing (NLP) is all about leveraging tools, techniques, and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech, and so on. In this book, we will be looking at tried and tested strategies—techniques and workflows—that can be leveraged by practitioners and data scientists to extract useful insights from text data.Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity expected of any data scientist in today’s fast-paced world! Text Analytics with Python is a practitioner’s guide to learning and applying NLP techniques to extract actionable insights from noisy and unstructured text data.This book helps its readers understand essential concepts in NLP along with extensive case studies and hands-on examples to master state-of-the-art tools, techniques, and frameworks for actually applying NLP to solve real-world problems. We leverage Python 3 and the latest and best state-of-the-art frameworks, including NLTK, Gensim, spaCy, Scikit-Learn, TextBlob, Keras, and TensorFlow, to showcase the examples in the book.You can find all the examples used in the book on GitHub at my journey in this field so far, I have struggled with various problems, faced many challenges, and learned various lessons over time. This book contains a major chunk of the knowledge I’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. Perhaps the biggest problem with regard to learning text analytics is not a lack of information but too much information, often called information overload.There are so many resources, documentation, papers, books, and journals containing so much content that they often overwhelm someone new to the field. You might have had questions like, “What is the right technique to solve a problem?,” “How does text summarization really work?,” and “Which frameworks are best for solving multi-class text categorization?,” among many others! By combining mathematical and theoretical concepts with practical implementations of real-world case studies using Python, this book tries to address this problem and help readers avoid the pressing issues I’ve faced in my journey so far.This book follows a comprehensive and structured approach. First it tackles the basics of natural language understanding and Python for handling text data in the initial chapters. Once you’re familiar with the basics, we cover text processing, parsing, and understanding. Then, we address interesting problems in text analytics in each of the remaining chapters, including text classification, clustering and similarity analysis, text summarization and topic models, semantic analysis and named entity recognition, and sentiment analysis and model interpretation. The last chapter is an interesting chapter on the recent advancements made in NLP thanks to deep learning and transfer learning and we cover an example of text classification with universal sentence embeddings.The idea of this book is to give you a flavor of the vast landscape of text analytics and NLP and to arm you with the necessary tools, techniques, and knowledge to tackle your own problems. I hope you find this book helpful and wish you the very best in your journey through the world of text analytics and NLP!