Digital Scholarship

Guide to coding qualitative data.

Provides an introduction to text mining using some of the most popular and powerful open-source tools: KNIME, RapidMiner, Weka, R, and Python.

Introduces core natural language processing (NLP) technologies to non-experts, describing the basic NLP processing tools, how Semantic Web and NLP technologies can enhance each other, and applications of these tools, focusing on semantic search and visualization, modeling user behavior, and an outlook on the future. Also covers sentiment analysis and the difficulties of performing NLP on social media, as well as some proposed solutions. Includes examples from the open source software GATE.

Covers the formulation of research questions through to the interpretation of findings. Includes chapters on specific social media platforms such as Twitter, Sina Weibo and Instagram, specific tools such as R and GATE, as well as critical chapters.

Learn how to write Python programs that work with large collections of unstructured text by following examples and exercises.

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. You'll learn how tidytext, natural language processing, sentiment analysis, and other tidy tools in R can make text analysis easier and more effective. Includes case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages.

Provides explanations of principles of time-proven machine learning algorithms applied in text mining together with step-by-step demonstrations of how to reveal the semantic contents in real-world datasets using the popular R-language with its implemented machine learning algorithms.