This book offers practical tools in Python to students of innovation, as well as competitive intelligence professionals, to track new developments in science, technology, and innovation. The book will appeal to both—tech-mining and data science audiences. For tech-mining audiences, Python presents an appealing, all-in-one language for managing the tech-mining process. The book is a complement to other introductory books on the Python language, providing recipes with which a practitioner can grow a practice of mining text. For data science audiences, this book gives a succinct overview over the most useful techniques of text mining. The book also provides relevant domain knowledge from engineering management; so, an appropriate context for analysis can be created. This is the first book of a two-book series. This first book discusses the mining of text, while the second one describes the analysis of text. This book describes how to extract actionable intelligence from a variety of sources including scientific articles, patents, pdfs, and web pages. There is a variety of tools available within Python for mining text. In particular, we discuss the use of pandas, BeautifulSoup, and pdfminer.
