![]() … The book is accompanied by a software implementation of the main algorithmic practices introduced. … The uniqueness of this book is the recourse to the prediction problem, which, by providing practical advice, allows for the integration of related topics. “This well-written work, which offers a unifying view of text mining through a systematic introduction to solving real-world problems. … Without a doubt, the authors’ experience in the field makes this book a successful contribution to the literature that targets the interests of the IT community and beyond.” (Jolanta Mizera-Pietraszko, ACM Computing Reviews, June, 2011) It seems to address all of the topics related to the fields that, when integrated, are known as knowledge engineering. ![]() “This is a well-written and interesting text for information technology (IT) professionals and computer science students. It would be an invaluable resource to any advanced undergraduate student interested in information retrieval." (Patrick Oladimeji, Times Higher Education, ) "This is a practical, up-to-date account of the various techniques for dealing intelligently with free text. Features: includes chapter summaries and exercises explores the application of each method provides several case studies contains links to free text-mining software. ![]() The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. ![]()
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