Fundamentals of predictive text mining
Author(s)
Bibliographic Information
Fundamentals of predictive text mining
（Texts in computer science）
Springer, 2015
2nd ed
- : softcover
Available at / 1 libraries
-
No Libraries matched.
- Remove all filters.
Note
Includes bibliographical references and index
Description and Table of Contents
Description
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. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. 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. 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. Features: includes chapter summaries and exercises; explores the application of each method; provides several case studies; contains links to free text-mining software.
Table of Contents
Overview of Text Mining
From Textual Information to Numerical Vectors
Using Text for Prediction
Information Retrieval and Text Mining
Finding Structure in a Document Collection
Looking for Information in Documents
Data Sources for Prediction: Databases, Hybrid Data and the Web
Case Studies
Emerging Directions
by "Nielsen BookData"
