Gabe Ignatow & Rada F. Mihalcea 
Text Mining [EPUB ebook] 
A Guidebook for the Social Sciences

Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it.
Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.

İçerik tablosu

Part I: Digital Texts, Digital Social Science

1. Social Science and the Digital Text Revolution

Learning Objectives

Introduction

History of Text Analysis

Risk and Rewards of Text Mining for the Social Sciences

Social Data from Digital Environments

Theory and Metatheory

Ethics of Text Mining

Organization of This Volume

2. Research Design Strategies

Learning Objectives

Introduction

Levels of Analysis

Strategies for Document Selection and Sampling

Types of Inferential Logic

Approaches to Research Design

Part II: Text Mining Fundamentals

3. Web Crawling and Scraping

Learning Objectives

Introduction

Web Statistics

Web Crawling

Web Scraping

Software for Web Crawling and Scraping

4. Lexical Resources

Learning Objectives

Introduction

Word Net

Roget′s Thesaurus

Linguistic Inquiry and Word Count

General Inquirer

Wikipedia

Downloadable Lexical Resources and APIs

5. Basic Text Processing

Learning Objectives

Introduction

Tokenization

Stopword Removal

Stemming and Lemmatization

Text Statistics

Language Models

Other Text Processing

Software for Text Processing

6. Supervised Learning

Learning Objectives

Feature Representation and Weighting

Supervised Learning Algorithms

Evaluation of Supervised Learning

Software for Supervised Learning

Part III: Text Analysis Methods from the Humanities and Social Sciences

7. Thematic Analysis, QDAS, and Visualization

Learning Objectives

Thematic Analysis

Qualitative Data Analysis Software

Visualization Tools

8. Narrative Analysis

Learning Objectives

Introduction

Conceptual Foundations

Mixed Methods of Narrative Analysis

Automated Approaches to Narrative Analysis

Future Directions

Specialized Software for Narrative Analysis

9. Metaphor Analysis

Learning Objectives

Introduction

Theoretical Foundations

Qualitative Metaphor Analysis

Mixed Methods of Metaphor Analysis

Automated Metaphor Identification Methods

Software for Metaphor Analysis

Part IV: Text Mining Methods from Computer Science

10. Word and Text Relatedness

Learning Objectives

Introduction

Theoretical Foundations

Corpus-based and Knowledge-based Measures of Relatedness

Software and Datasets for Word and Text Relatedness

Further Reading

11. Text Classification

Learning Objectives

Introduction

Applications of Text Classification

Representing Texts for Supervised Text Classification

Text Classification Algorithms

Bootstrapping in Text Classifcation

Evaluation of Text Classification

Software and Datasets for Text Classification

12. Information Extraction

Learning Objectives

Introduction

Entity Extraction

Relation Extraction

Web Information Extraction

Template Filling

Software and Datasets for Information Extraction and Text Mining

13. Information Retrieval

Learning Objectives

Introduction

Theoretical Foundations

Components of an Information Retrieval System

Information Retrieval Models

The Vector-Space Model

Evaluation of Information Retrieval Models

Web-Based Information Retrieval

Software and Datasets for Information Retrieval

14. Sentiment Analysis

Learning Objectives

Introduction

Theoretical Foundations

Lexicons

Corpora

Tools

Future Directions

Software and Datasets for Word and Text Relatedness

15. Topic Models

Learning Objectives

Introduction

Digital Humanities

Political Science

Sociology

Software for Topic Modeling

V: Conclusions

16. Text Mining, Text Analysis, and the Future of Social Science

Introduction

Social and Computer Science Collaboration

Yazar hakkında

Rada Mihalcea is a professor of computer science and engineering at the University of Michigan. Her research interests are in computational linguistics, with a focus on lexical semantics,  multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the following journals: Computational Linguistics, Language Resources and Evaluation, Natural Language Engineering,  Research on Language and Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a general chair for the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL, 2015) and a program cochair for the Conference of the Association for Computational Linguistics (2011) and the Conference on Empirical Methods in Natural Language Processing (2009). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.