NVivo 11 Essentials is a comprehensive guide to the world's most popular qualitative data analysis software. Provides instruction to NVivo users of all skill levels and experience with both qualitative data analysis and qualitative research methods. Provides practical, anecdotal advice for using NVivo 11 for every stage of your research project.
This straightforward, jargon-free book provides an invaluable introduction to planning and conducting qualitative data analysis with NVivo. This second edition contains new chapters on handling a literature review, visualizing data, working in mixed methods and social media datasets, and approaching NVivo as a team.
Leximancer is text mining software that can be used to analyse the content of collections of textual documents and to visually display the extracted information in a browser. The information is displayed by means of a conceptual map that provides an overview of the material, representing the main concepts contained within the text and how they are related.
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.
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.
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.
Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem.