Bhargav Srinivasa Desikan

A deluge of digital content is generated daily by web-based platforms and sensors that capture digital traces of communication and connection, and complex states of society, the economy, and the world. Emerging deep learning methods enable the integration and analysis of these complex data in order to address real world problems by designing and discovering successful solutions.The real power of deep learning is unleashed by thinking with deep learning to reformulate and solve problems traditional machine learning methods cannot address. These include fusing diverse data like text, images, tabular and network data into integrated and comprehensive digital doubles of the scenarios you want to model, the generation of promising recommendations, and the creation of AI assistants that radically augment an analyst or system's intelligence. This book uses Python and the widely popular PyData ecosystem to demonstrate all motivating examples and includes working code, accompanying exercises, relevant datasets and additional analytics and visualization that facilitate interpretation, communication and decision making.

Work with Python and powerful open source tools such as Gensim and spaCy to perform modern text analysis, natural language processing, and computational linguistics algorithms.Key FeaturesDiscover the open source Python text analysis ecosystem, using spaCy, Gensim, scikit-learn, and KerasHands-on text analysis with Python, featuring natural language processing and computational linguistics algorithmsLearn deep learning techniques for text analysisBook DescriptionModern text analysis is now very accessible using Python and open source tools, so discover how you can now perform modern text analysis in this era of textual data.This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now - with Python, and tools like Gensim and spaCy.You'll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You're then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You'll learn to tag, parse, and model text using the best tools. You'll gain hands-on knowledge of the best frameworks to use, and you'll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You'll discover the rich ecosystem of Python tools you have available to conduct NLP - and enter the interesting world of modern text analysis.What you will learn Why text analysis is important in our modern age Understand NLP terminology and get to know the Python tools and datasetsLearn how to pre-process and clean textual dataConvert textual data into vector space representationsUsing spaCy to process text Train your own NLP models for computational linguisticsUse statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learnEmploy deep learning techniques for text analysis using KerasWho this book is forThis book is for you if you want to dive in, hands-first, into the interesting world of text analysis and NLP, and you're ready to work with the rich Python ecosystem of tools and datasets waiting for you!