Data Mining Methods for the Content Analyst ─ An Introduction to the Computational Analysis of Content

With continuous advancements and an increase in user popularity, data mining technologies serve as an invaluable resource for researchers across a wide range of disciplines in the humanities and social sciences. In this comprehensive guide, author and research scientist Kalev Leetaru introduces the approaches, strategies, and methodologies of current data mining techniques, offering insights for new and experienced users alike.
Designed as an instructive reference to computer-based analysis approaches, each chapter of this resource explains a set of core concepts and analytical data mining strategies, along with detailed examples and steps relating to current data mining practices. Every technique is considered with regard to context, theory of operation and methodological concerns, and focuses on the capabilities and strengths relating to these technologies. In addressing critical methodologies and approaches to automated analytical techniques, this work provides an essential overview to a broad innovative field.

Kalev Leetaru is Senior Research Scientist for Content Analysis at the University of Illinois Institute for Computing in Humanities, Arts, and Social Science and Center Affiliate of the National Center for Supercomputing Applications. He leads a number of large initiatives centering on the application of high performance computing to grand challenge problems using massive-scale document and data archives.

Chapter 1 - Introduction


What Is Content Analysis?


Why Use Computerized Analysis Techniques?


Standalone Tools Or Integrated Suites


Transitioning From Theory To Practice


Chapter 2 - Obtaining And Preparing Data



Collecting Data From Digital Text Repositories



Are The Data Meaningful?


Using Data In Unintended Ways


Analytical Resolution


Types Of Data Sources


Finding Sources


Searching Text Collections


Sources Of Incompleteness


Licensing Restrictions And Content Blackouts


Measuring Viewership


Accuracy And Convenience Samples


Random Samples


Multimedia Content



Converting To Textual Format


Prosody


Example Data Sources



Patterns In Historical War Coverage


Competitive Intelligence


Global News Coverage


Downloading Content



Digital Content


Print Content


Preparing Content



Document Extraction


Cleaning


Post Filtering


Reforming/Reshaping


Content Proxy Extraction


Chapter 3 - Vocabulary Analysis



The Basics



Word Histograms


Readability Indexes


Normative Comparison


Non-Word Analysis


Colloquialisms: Abbreviations And Slang


Restricting The Analytical Window


Vocabulary Comparison And Evolution / Chronemics


Advanced Topics



Syllables, Rhyming, And ‘Sounds Like’


Gender And Language


Authorship Attribution


Word Morphology, Stemming, And Lemmatization


Chapter 4 – Correlation And Co-Occurrence



Understanding Correlation


Computing Word Correlations


Directionality


Concordance


Co-Occurrence And Search


Language Variation And Lexicons


Non-Co-Occurrence


Correlation With Metadata


Chapter 5 – Lexicons, Entity Extraction, And Geocoding



Lexicons



Lexicons And Categorization


Lexical Correlation


Lexicon Consistency Checks


Thesauri And Vocabulary Expanders


Named Entity Extraction



Lexicons And Processing


Applications


Geocoding, Gazetteers, And Spatial Analysis



Geocoding


Gazetteers And The Geocoding Process


Operating Under Uncertainty


Spatial Analysis


Chapter 6 – Topic Extraction



How Machines Process Text



Unstructured Text


Extracting Meaning From Text


Applications Of Topic Extraction



Comparing/Clustering Documents


Automatic Summarization


Automatic Keyword Generation


Multilingual Analysis: Topic Extraction With Multiple Languages


Chapter 7 – Sentiment Analysis



Examining Emotions



Evolution


Evaluation


Analytical Resolution: Documents vs Objects


Hand-Crafted vs Automatically-Generated Lexicons


Other Sentiment Scales


Limitations


Measuring Language Rather Than Worldview


Chapter 8 – Similarity, Categorization and Clustering



Categorization



The Vector-Space Model


Feature Selection


Feature Reduction


Learning Algorithm


Evaluating ATC Results


Benefits of ATC Over Human Categorization


Limitations of ATC


Applications of ATC


Clustering



Automated Clustering


Hierarchical Clustering


Partitional Clustering


Document Similarity



Vector Space Model


Contingency Tables


Chapter 9 – Network Analysis



Understanding Network Analysis


Network Content Analysis


Representing Network Data


Constructing the Network


Network Structure


The Triad Census


Network Evolution


Visualization and Clustering