Text mining and text analysis

Software is cut and dried, every button you press has a predictable effect, but qualitative analysis is open ended and unfolds in unpredictable ways. This contradiction is best resolved by separating analytic strategies, what you plan to do, from software tactics, how you plan to do it. Expert NVivo users have unconsciously learned to do this. The Five-Level QDA, method unpacks the process so that you can learn it consciously and efficiently.

Hermeneutica introduces text analysis using computer-assisted interpretive practices. It offers theoretical chapters about text analysis, presents a set of analytical tools (called Voyant) that instantiate the theory, and provides example essays that illustrate the use of these tools. Voyant allows users to integrate interpretation into texts by creating word clouds and complex data journalism interactives.

Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you'll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr.

Text Analysis Chapter: In this chapter, several methods for extracting meaning from a collection of parsed textual documents are presented. Examples include information retrieval, topic modeling, and stylometrics. Particular focus is placed on how to use these methods for constructing visualizations of textual corpora and a high-level categorization of some narrative trends.

This book teaches readers to integrate data analysis techniques into humanities research practices using the R programming language. Methods for general-purpose visualization and analysis are introduced first, followed by domain-specific techniques for working with networks, text, geospatial data, temporal data, and images. The second edition of the text is a significant revision, with almost every aspect of the text rewritten in some way. The most notable difference is the incorporation of new R packages such as ggplot2 and dplyr that center broad data-science concepts.

This book takes a practical, hands-on approach to teaching you a reliable, cost-effective approach to mining the vast, untold riches buried within all forms of text using R.

The second edition of this book will show you how to use the latest state-of-the-art frameworks in Natural Language Processing, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python.

Specifically designed for linguists, this book provides an introduction to programming using Python for those with little to no experience of coding. More experienced users of Python will also benefit from the advanced chapters on graphical user interfaces and functional programming.