Free Text Mining, Text Analysis, Text Analytics Books 4.7 (93.33%) 3 ratings (93.33%)ratings

Free Text Mining, Text Analysis, Text Analytics Books: Text Mining is the process of discovering unknown information, by an automatic process of extracting the information from a large data set of different unstructured textual resources. Text analysis uses many linguistic, statistical, and machine learning techniques. Text Mining is synonymous with Text Analytics. Text Mining tasks include text categorization, text clustering, concept and entity extraction, granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.Text Analytics is applied for a wide variety of government, research, and business needs including enterprise business intelligence, data mining, competitive intelligence, e-discovery, records management, national security intelligence and scientific discovery.

Free Text Mining, Text Analysis, Text Analytics Books

Theory and Applications for Advanced Text Mining

Edited by Shigeaki Sakurai

This book is composed of 9 chapters introducing advanced text mining techniques. There are various techniques from relation extraction to under or less resourced language.

Theory and Applications for Advanced Text Mining by Shigeaki Sakurai

Data-Intensive Text Processing with MapReduce

by Jimmy Lin (Author) , Chris Dyer (Author) , Graeme Hirst (Series Editor)

This book discusses MapReduce Basics, MapReduce Algorithm Design, Inverted Indexing for Text Retrieval, Graph Algorithms and EM Algorithms for Text Processing.

Data-Intensive Text Processing with MapReduce by Jimmy Lin , Chris Dyer, Graeme Hirst

Text Mining (Big Data, Unstructured Data)

Statsoft’s electronic book provides an introductory overview, typical applications for Text mining,approaches to text mining, issues and considerations for "numericizing" text,transforming word frequencies, latent semantic indexing via singular value decomposition and incorporating text mining results in data mining projects.

Text Mining (Big Data, Unstructured Data)

Natural Language Processing with Python

- Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper

This book provides a highly accessible introduction to the field of NLP. It can be used for individual study or as the textbook for a course on natural language processing or computational linguistics, or as a supplement to courses in artificial intelligence, text mining, or corpus linguistics. The book is based on the Python programming language together with an open source library called the Natural Language Toolkit (NLTK).

Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper

Introduction to Information Retrieval

by Christopher D. Manning (Author), Prabhakar Raghavan (Author), Hinrich Schütze (Author)

This book teaches web era information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. Written from a computer science perspective it gives an up to date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.

Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan , Hinrich Schütze

Foundations of Statistical Natural Language Processing

By Christopher Manning (Author), Hinrich Schuetze (Author)

This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

Foundations of Statistical Natural Language Processing By Christopher Manning , Hinrich Schuetze

A Programmer's Guide to Data Mining

by Ron Zacharski

This book is a hands-on guide on data mining, collective intelligence, and building recommendation systems. Chapter 7 explains the Naïve Bayes and unstructured text on how to use Naïve Bayes to classify unstructured text.

A Programmer's Guide to Data Mining by Ron Zacharski

Text Mining Applications and Theory

by Michael W. Berry, University of Tennessee, USA, Jacob Kogan, University of Maryland Baltimore County, USA

This book provides algorithms and techniques for critical tasks in text mining applications, such as clustering, classification, anomaly and trend detection, and stream analysis. Also Presents a survey of text visualization techniques and looks at the multilingual text classification problem.

Text Mining Applications and Theory by Michael W. Berry, Jacob Kogan

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